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] The present technology relates to an information processing apparatus, an information processing method, and a program, and more particularly to an information processing apparatus, an information processing method, and a program suitable for use in assisting the driving of a moving object.

Background Art

[0002] Conventionally, a driver's biometric information has been used to detect the driver's state during driving.

[0003] For example, it has been proposed to correct a measured value of a driver's skin impedance based on correction data based on an average value of measured values of the driver's skin impedance when the eyes are closed at rest and during normal activities, and to determine the driver's level of wakefulness 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 biometric sensor of a vehicle to a data processing device via a portable terminal installed in a charger in 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

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] By the way, the driving safety of a moving object such as a vehicle is affected by various factors in addition to the driver's state during driving. And it is desired to improve the driving safety of the moving object while considering those influences as much as possible.

[0007] This technology has been made in view of such a situation, and aims to improve the safety of the operation of a moving body.

Means for Solving the Problems

[0008] An information processing apparatus according to one aspect of the present technology includes a diagnosis unit that diagnoses the suitability of a user's driving of a moving body based on a pre-driving state of the user who drives the moving body acquired in advance and a state of the user who is driving acquired during driving, and a presentation control unit that generates feedback information based on the diagnosis obtained by the diagnosis unit.

[0009] The information processing apparatus further includes a driving behavior detection unit that detects a driving behavior, which is the behavior of the user or the moving body during driving, and the level of dangerous driving to be detected by the driving behavior detection unit can be changed based on the result of the diagnosis.

[0010] The diagnosis unit can further perform the diagnosis based on the detection result of the driving behavior.

[0011] The diagnosis unit can diagnose the suitability of the user's pre-driving based on the state of the user before driving, and can diagnose the suitability of the user's driving during driving based on the state of the user during driving and the detection result of the driving behavior.

[0012] The information processing apparatus can further include a risk prediction unit that predicts a risk related to the driving of the moving body by the user based on at least one of the result of the diagnosis and the detection result of the driving behavior.

[0013] The feedback information can include the content of the risk and the basis on which the risk is predicted.

[0014] The basis on which the risk is predicted can be based on the result of the diagnosis.

[0015] The feedback information can include the risk avoidance plan.

[0016] The lower the appropriateness of the user's driving based on the diagnosis, the lower the level of dangerous driving to be detected by the driving behavior detection unit can be.

[0017] A learning unit for performing learning of the model for making the diagnosis can be further provided based on the result of the diagnosis and the detection result of the driving behavior.

[0018] The diagnosis unit can be made to perform the diagnosis based on the difference between the user's most recent state and the user's standard state.

[0019] The diagnosis unit can further be made to perform the diagnosis based on the difference between the user's most recent state and the average state of users in a set of users including a plurality of users.

[0020] A state estimation unit for estimating the state of the user before and during driving is further provided, and the diagnosis unit can be made to perform the diagnosis based on the estimation result of the user's state.

[0021] The user's state can include at least one of the user's biological state, behavior, and emotion.

[0022] The diagnosis unit can be made to perform the diagnosis based on the data indicating the state of the user acquired before and during the user's driving.

[0023] The information processing method according to one aspect of the present technology includes a presentation control step of generating a risk avoidance plan for a user based on a predicted risk, an evaluation step of evaluating the compliance of the user based on the reaction of the user to the risk avoidance plan, and a risk prediction step of adjusting a risk prediction parameter based on the evaluated compliance.

[0024] The program of one aspect of the present technology causes a computer to execute a process including a presentation control step of generating a risk avoidance plan for a user based on a predicted risk, an evaluation step of evaluating the user's compliance based on the user's reaction to the risk avoidance plan, and a risk prediction step of adjusting a risk prediction parameter based on the evaluated compliance.

[0025] In one aspect of the present technology, a risk avoidance plan for a user is generated based on a predicted risk, the user's compliance is evaluated based on the user's reaction to the risk avoidance plan, and a risk prediction parameter is adjusted based on the evaluated compliance.

Advantages of the Invention

[0026] According to one aspect of the present technology, the diagnostic accuracy of the suitability of a user's vehicle driving is improved. As a result, the safety of vehicle driving is improved.

[0027] Note that the effects described here are not necessarily limited, and any of the effects described in the present disclosure may be applicable.

Brief Description of the Drawings

[0028]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Embodiments for Carrying Out the Invention

[0029] Hereinafter, embodiments for carrying out the present technology will be described. The description will be made in the following order. 1. Embodiment 2. Variation 3. Others

[0030] <<1. Embodiment>> <Configuration Example of Information Processing System> 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 provides services such as vehicle driving support and automobile insurance, for example, using telematics technology.

[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 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 consists of one or more information processing terminals held 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] Server 13 communicates with the user terminal unit 11 and the vehicle 12 via the network 14, and provides services such as driving support and automobile insurance to users who use the information processing system 10.

[0036] In addition, in FIG. 1, for the sake of clarity, the user terminal unit 11, the vehicle 12, and the server 13 are each illustrated one by one, but it is possible to provide two or more of them. For example, the user terminal unit 11 and the vehicle 12 are provided in approximately the same number as the number of users who use the information processing system 10.

[0037] In addition, hereinafter, for the sake of simplicity of explanation, the description of "via the network 14" when the user terminal unit 11, the vehicle 12, and the server 13 communicate via the network 14 is omitted.

[0038] <Configuration example of user terminal unit> FIG. 2 is a block diagram showing a configuration example 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 composed of, for example, a portable information processing terminal such as a smartphone, a mobile phone, a tablet, a notebook personal computer, a portable game machine, a portable video or music playback device, etc.

[0040] The mobile terminal 51 includes a GNSS (Global Navigation Satellite System) receiver 61, an inertial sensor 62, an environmental sensor 63, a biological sensor 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 (the user having it) by receiving radio waves from positioning satellites, and supplies position data indicating the measured current position to the control unit 67.

[0042] The inertial sensor 62 detects various inertial data regarding the mobile terminal 51 (the user having it), 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, etc.

[0043] The environmental sensor 63 detects various environmental data around the mobile terminal 51 (the user having it), 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, carbon dioxide concentration, etc.

[0044] The biological sensor 64 detects various biological data of the user, and supplies the detected biological data to the control unit 67. The biological data detected by the biological sensor 64 includes, for example, one or more of heart rate, sweating amount, blood pressure, blood oxygen concentration, electromyogram, body temperature, body composition, alcohol concentration in exhaled breath, maximum oxygen uptake, calorie consumption, voice tone, conversation speed, etc.

[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, etc.

[0047] The control unit 67 includes a control device such as various processors, for example. The control unit 67 controls each part of the mobile terminal 51 and performs various processes based on the data supplied from the GNSS receiver 61, the inertial sensor 62, the environmental sensor 63, the biological sensor 64, and the input unit 65, as well as the data received from the outside via the communication unit 68. Also, the control unit 67 supplies the data obtained by various processes to the output unit 66 or transmits it 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.) by a predetermined communication method. Any wireless or wired communication method can be adopted for the communication method of the communication unit 68. Also, the communication unit 68 can support multiple communication methods.

[0049] The wearable terminal 52 is composed of a wearable terminal in any form such as, for example, glasses type, wristwatch type, bracelet type, necklace type, neckband type, earphone type, headset type, and head-mounted type.

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

[0051] Similar to the biological sensor 64 of the mobile terminal 51, the biological sensor 81 detects various biological data of the user and supplies the detected biological data to the control unit 84. Note that the types of biological data detected by the biological sensor 81 and the types of biological data detected by the biological sensor 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, etc.

[0054] The control unit 84 includes, for example, a control device such as various processors. Based on the data supplied from the biological sensor 81 and the input unit 82, as well as the data received from the outside via the communication unit 85, etc., the control unit 84 controls each part of the wearable terminal 52 and performs various processes. Also, the control unit 84 supplies the data obtained by various processes to the output unit 83 or transmits it 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.) by a predetermined communication method. Any wireless or wired method can be adopted for the communication method of the communication unit 85. Also, the communication unit 85 can support a plurality of communication methods.

[0056] <Configuration example of a vehicle> FIG. 3 is a block diagram showing a configuration example of a part 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 116.

[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, vehicle speed, torque, steering angle, yaw angle, gear state, side brake state, accelerator pedal depression amount, brake pedal depression amount, direction indicator state, light state, tire rotation angle and rotation speed, data indicating the diagnostic result of OBD (On-board Diagnostics) (hereinafter referred to as OBD data), and sensor data such as millimeter wave radar and laser radar, etc., including one or more of them.

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

[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 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, etc.

[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 the data supplied from the vehicle data acquisition unit 111, the video and audio acquisition unit 112, and the input unit 113, as well as the data received from the outside via the communication unit 116. Also, the control unit 115 supplies the data obtained by various processes to the output unit 114 or transmits it to other devices via the communication unit 116.

[0062] The communication unit 116 communicates with other devices (e.g., the server 13, the mobile terminal 51, the wearable terminal 52, etc.) by a predetermined communication method. Any wireless or wired communication method can be adopted for the communication method of the communication unit 116. Also, the communication unit 116 can support a plurality of communication methods.

[0063] <Configuration example of the server> FIG. 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 peripheral data acquisition unit 153, a diagnosis unit 154, a driving behavior detection unit 155, a risk prediction unit 156, a damage 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 by a predetermined communication method. Any wireless or wired communication method can be adopted for the communication method of the communication unit 151. Further, the communication unit 151 can also support a plurality of 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 it in the storage unit 162.

[0066] Further, the state estimation unit 152 performs an estimation process of the user's state 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 for estimating the user's state 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 user's state, and stores it in the storage unit 162.

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

[0068] The diagnosis unit 154 acquires the estimated state history of the user, the driving behavior history, and the driving diagnosis model from the storage unit 162. Here, the driving behavior history is a history of detection results of driving behaviors, which are the behaviors of the user or the vehicle 12 during driving, and is generated for each user, for example. Also, the driving diagnosis model is a model used for driving diagnosis to diagnose the suitability of the user's driving of the vehicle, and is generated for each user, for example. The diagnosis unit 154 performs a 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. Also, the driving behavior detection unit 155 acquires the estimated state history of the user and the driving behavior detection model from the storage unit 162. Here, the driving behavior detection model is a model used for detecting driving behaviors, and is generated for each user, for example. Further, the driving behavior detection unit 155 acquires, from the damage prediction unit 157, the risk related to the user's driving of the vehicle and the prediction result of the damage caused by the risk. The driving behavior detection unit 155 performs a detection process of the user's driving behavior using the driving behavior detection model based on the acquired history and data, etc. The driving behavior detection unit 155 generates and updates a driving behavior history, which is a history of the detection results of the user's driving behavior, and stores it in the storage unit 162.

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

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

[0072] The presentation control unit 158 acquires the driving diagnosis history of the user 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 including information regarding the predicted risk and presented to the user, based on the prediction results of the risk and the damage, as well as the driving diagnosis history and the insurance premium of the user. 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. Further, the presentation control unit 158 supplies the feedback information to the evaluation unit 159.

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

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

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

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

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

[0078] <Driving support process> Next, with reference to the flowcharts of FIGS. 5 and 6, the driving support process executed by the server 13 will be described.

[0079] Note that hereinafter, the description will mainly focus on the process for a single specific user (hereinafter referred to as the target user), but in reality, the processes for other users other than the target user are performed in parallel.

[0080] In step S1, the server 13 starts the estimation process of the state of the user (target user) when not driving. Specifically, for example, the following processes are started.

[0081] The state estimation unit 152 acquires data related to the state of the target user from among the data (for example, inertial data, environmental data, biological data, input data, etc.) received by the communication unit 151 from the user terminal unit 11 of the target user. The state estimation unit 152 stores each piece of data in the storage unit 162 together with the time when the data was acquired. Thereby, the state data log of the target user is updated.

[0082] Note that the state data log of the target user may include not only data related to the state of the target user himself / herself but also data related to the state of the surroundings of the target user.

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

[0084] For example, as illustrated in FIG. 7, the state estimation unit 152 estimates the biological state and behavior of the target user for each frame of a predetermined time based on the time-series change of the sensor data group indicating the state of the target user. For example, various biological states of the target user such as concentration, arousal level, fatigue level, stress level, tension level, and intensity of exercise are estimated based on the heart rate and sweating amount of the target user. For example, the type of behavior of the target user (for example, stationary, walking, running, cycling, going up and down stairs, eating, sleeping, etc.) is estimated based on the acceleration and angular velocity of the target user and the ambient air pressure.

[0085] Also, for example, the state estimation unit 152 estimates the moving distance, moving speed, action range, etc. of the target user based on the position data and acceleration of the target user.

[0086] Furthermore, the state estimation unit 152 estimates the emotion of the target user based on the biological data of the target user and the estimation results of the biological state of the target user. For example, the state estimation unit 152 estimates the degrees of joy, anger, sorrow, and pleasure, excitement level, anxiety level, restlessness level, etc. of the target user.

[0087] Note that the biological state and emotion of the target user are not necessarily all clearly distinguishable, and there are also overlapping ones. For example, the excitement level can be either the biological state or the emotion of the target user.

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

[0089] The state estimation unit 152 stores the estimated state of the target user in the storage unit 162 together with the estimated time. Thereby, the estimated state history of the target user is updated.

[0090] Note that, for example, as illustrated in FIG. 7, the estimated state history includes data in which numerical values for each frame such as the arousal level are arranged in time series, and data in which labels (indicating the type of action) assigned for each frame such as actions are arranged in time series.

[0091] In step S2, the learning unit 160 determines whether to learn the pattern of the standard state (standard state pattern) of the user (target user). For example, the learning of the standard state pattern of the target user is executed at a predetermined timing such as when the target user starts using the information processing system 10, every time a predetermined time elapses, or every time the data amount of the estimated state history of the target user increases by a predetermined amount or more. Then, when it is determined that the current timing is for learning the standard state pattern of the target user, the learning unit 160 determines to learn the standard state pattern of the target user, and the process proceeds to step S3.

[0092] In step S3, the learning unit 160 learns the pattern of the standard state (standard state pattern) of the user (target user). For example, as illustrated in FIG. 8, the learning unit 160 learns the standard state pattern of the target user based on the estimated state history of the target user within a relatively long period of time (e.g., monthly, annually) in the recent past.

[0093] For example, as the standard state pattern of the target user, the learning unit 160 calculates the average and variance, etc. for each period of a predetermined time (e.g., 1 minute, 10 minutes, 30 minutes, or 1 hour) of the following items, thereby learning the transition pattern indicating the standard transition of each item for a predetermined period (e.g., 1 day). For example, the standard transition pattern for one day of the concentration, arousal level, fatigue level, stress level, tension level, intensity of exercise, heart rate, sweating amount, amount of exercise, degree of mood (joy, anger, sorrow, and happiness), excitement level, anxiety level, etc. of the target user is learned.

[0094] Also, for example, as the standard state pattern of the target user, the learning unit 160 calculates the average and variance, etc. for each day (each day of the week) of the following items, thereby learning the standard transition pattern of each item per a predetermined period (e.g., one week). For example, the standard transition pattern for one week of the sleep time, wake-up time, bedtime, amount of exercise, moving range, number of meals, meal time, meal time zone, driving time, driving time zone, commuting time zone, school commuting time zone, etc. of the target user is learned.

[0095] In addition, the learning unit 160 learns the average standard state pattern (user set state pattern) within the user set based on the estimated state history of each user within a predetermined user set as a comparison target for the standard state pattern of the target user.

[0096] Here, the user set may include all users of the information processing system 10, or may include only some users. In the latter case, for example, it may be a user set consisting of users similar to the target user. Here, a user similar to the target user is, for example, a user whose attributes (such as age, gender, occupation, address, etc.), behavior patterns, preferences, etc. are similar to those of the target user. Also, the target 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 set state pattern by aggregating the averages of the standard state patterns of each user within the user set. For example, by aggregating the average of the standard transition patterns of each item over one day within the user set, the average one-day transition pattern of each item within the user set is learned. Also, for example, by aggregating the average of the standard transition patterns of each item over one week within the user set, the average one-week transition pattern of each item within the user set is learned.

[0098] The learning unit 160 causes the storage unit 162 to store the standard state pattern of the target user and the data indicating the learning result of the user set state pattern.

[0099] Note that the timings of learning the standard state pattern of the target user and learning the user set state pattern do not necessarily need to be synchronized, and they may be performed at different timings.

[0100] Thereafter, the process proceeds to step S4.

[0101] On the other hand, in step S2, if it is determined not to learn the standard state pattern of the target user, 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 during non-driving. For example, based on the estimated state history of the target user, the diagnosis unit 154 detects a transition pattern (hereinafter referred to as the recent state pattern) within a relatively short period (e.g., one day and one week) for the same items as the above-described standard state pattern.

[0103] Next, the diagnosis unit 154 calculates, for example, as illustrated in FIG. 10, a divergence degree vector x indicating the divergence degree between the recent state pattern of the target user and the standard state pattern, and a divergence degree vector y indicating the divergence degree between the recent state pattern of the target user and the user set state pattern. For example, the divergence degree vector x and the divergence degree vector y are vectors including values obtained by normalizing the divergence degree for each item of the two state patterns to be compared.

[0104] Then, the diagnosis unit 154 calculates the driving suitability degree u of the target user during non-driving using the driving diagnosis model of the target user represented by the function f of the following formula (1).

[0105] u = f(x, y, wx, wy) ···(1)

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

[0107] The driving suitability degree u increases as the divergence degree of each item included in the divergence degree vector x decreases. That is, the closer the recent state pattern of the target user is to the standard state pattern, in other words, the smaller the difference between the recent state of the target user and the standard state, the more suitable the state is determined to be for driving. On the other hand, the driving suitability degree u decreases as the divergence degree of each item included in the divergence degree vector x increases. That is, the farther the recent state pattern of the target user is from the standard state pattern, in other words, the larger the difference between the recent state of the target user and the standard state, the less suitable the state is determined to be for driving.

[0108] Also, the driving suitability degree u increases as the degree of deviation of each item included in the deviation vector y decreases. That is, the closer the most recent state pattern of the target user is to the user set state pattern, in other words, the smaller the difference between the most recent state of the target user and the average state in the user set, the more suitable the driving state is determined to be. On the other hand, the driving suitability degree u decreases as the degree of deviation of each item included in the deviation vector y increases. That is, the farther the most recent state pattern of the target user is from the user set state pattern, in other words, the larger the difference between the most recent state of the target user and the average state in the user set, the less suitable the driving state is determined to be.

[0109] Note that as the weight wx increases, the influence of the deviation vector x on the driving suitability degree u (that is, the difference between the most recent state pattern of the target user and the standard state pattern) increases. On the other hand, as the weight wy increases, the influence of the deviation vector y on the driving suitability degree u (that is, the difference between the user set state pattern of the target user and the standard state pattern) increases.

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

[0111] For example, when the sleep time in the most recent state pattern of the target user is significantly lower than the standard state pattern or the user set average pattern, it is estimated that sleep deprivation is the factor causing the decrease.

[0112] For example, when one or more of the exercise time, heart rate, and sweating amount in the recent state pattern of the target user significantly exceed the standard state pattern or the average pattern of the user group, it is presumed that physical fatigue due to intense exercise is a contributing factor to the decline.

[0113] For example, when one or more of the stress level, tension level, and anxiety level in the recent state pattern of the target user significantly exceed the standard state pattern or the average pattern of the user group, it is presumed that the target user's anxiety is a contributing factor to the decline.

[0114] Note that when the driving suitability degree u is equal to or higher than a predetermined threshold value, for example, there is no particular contributing factor to the decline in the driving suitability degree u.

[0115] The diagnosis unit 154 stores the driving suitability degree u and the presumed contributing factor to the decline, together with the time of diagnosis, in the storage unit 162 as the diagnosis result of the driving suitability of the target user. Thereby, the driving diagnosis history of the target user is updated.

[0116] In step S5, the risk prediction unit 156 performs risk prediction. Specifically, the risk prediction unit 156 acquires the driving diagnosis history of the target user and the risk prediction model from the storage unit 162. Based on the driving diagnosis history of the target user, the risk prediction unit 156 uses the risk prediction model of the target user to predict the risk when the target user drives at the current time.

[0117] For example, when the contributing factor to the decline in the driving suitability degree u is lack of sleep or physical fatigue, it is presumed that there is a risk of dozing while driving.

[0118] For example, when the contributing factor to the decline in the driving suitability degree u is the anxiety of the target user, for example, there are risks such as collision or contact with other vehicles due to speeding or forced overtaking, and collision or contact with obstacles (for example, other vehicles, bicycles, pedestrians, etc.) due to the inattention of the target user.

[0119] In addition, the risk prediction unit 156 estimates the occurrence probability of the predicted risk. For example, the shorter the sleep time of the target user, the higher the estimated occurrence probability of dozing off while driving.

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

[0121] The risk prediction unit 156 supplies the prediction result of the risk to the damage prediction unit 157. This risk prediction result includes the predicted risk, the basis for predicting the risk (for example, the factor causing the decrease in 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 the damage (for example, risk / penalty value) caused by the risk predicted by the risk prediction unit 156. At this time, the occurrence probability of the risk is taken into account. That is, the higher the occurrence probability, the greater the predicted damage, and the lower the occurrence probability, the smaller the predicted damage. The damage prediction unit 157 supplies the prediction results of the 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 the detection parameters of the driving behavior. For example, the driving behavior detection unit 155 adjusts the parameters for detecting dangerous driving among various driving behaviors according to the predicted risk and damage.

[0124] Here, the driving behaviors related to the detection of dangerous driving are, for example, sudden starts, sudden accelerations, sudden brakes, and sudden steering and other sudden operations, weaving driving, dozing off while driving, decrease in wakefulness and concentration, distracted driving, inattention ahead, speeding, forced overtaking, insufficient inter-vehicle distance, and approaching obstacles and other dangerous driving behaviors.

[0125] Also, the detection parameters are adjusted so that the level (degree of risk) of dangerous driving to be detected becomes lower as the presumed risk and damage increase. As a result, dangerous driving can be detected at an earlier and milder stage.

[0126] For example, when the occurrence of drowsy driving is predicted, the threshold value of the number of blinks in drowsiness determination is decreased so as to detect the user's drowsiness earlier. For example, when the occurrence of distracted driving, inattentiveness ahead, speeding, etc. is predicted, the threshold value for detecting sudden acceleration or sudden braking is decreased so that sudden acceleration or sudden braking can be more easily detected.

[0127] Furthermore, the lower the level of dangerous driving to be detected, the lower the level (degree of risk) of the risk to be predicted. That is, risks at an earlier and milder stage can be predicted.

[0128] Here, the level of dangerous driving to be detected and the level of the risk to be predicted change according to the driving suitability degree u of the target user. For example, as described above, generally, the lower the driving suitability degree u, the greater the predicted risk and damage occurrence probability, and the greater 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 suitability degree u, the lower the level of dangerous driving to be detected and the level of the risk to be predicted.

[0129] In step S8, the presentation control unit 158 determines whether to provide feedback to the user (target user). The feedback to the target user is executed at a predetermined timing. For example, when a feedback request is sent from the user terminal unit 11 of the target user, the feedback is executed at a timing such as every time a predetermined time has elapsed or when a significant risk is predicted. Then, when the current timing is the timing to provide feedback to the target user, the presentation control unit 158 determines to provide feedback to the target user, and the process proceeds to step S9.

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

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

[0132] For example, when it is estimated that the target user is sleep-deprived, an audio message such as "You are sleep-deprived. It is recommended that you get some sleep before driving." is output.

[0133] Also, for example, as the driving diagnosis result of the target user, the driving suitability degree u is presented as a value at a predetermined level (e.g., 10 levels), and the basis therefor is presented.

[0134] Furthermore, for example, the estimated result of the target user's state may be presented. For example, the emotional indices such as the target user's joy, anger, sorrow, happiness, excitement level, and aggressiveness may be presented as values at a predetermined level (e.g., 10 levels).

[0135] Thereafter, the process proceeds to step S10.

[0136] On the other hand, in step S8, if it is determined not to provide feedback to the target 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 the user (target user) has started driving. Specifically, the state estimation unit 152 determines whether the target user has started driving based on the data received from at least one of the user terminal unit 11 and the vehicle 12 of the target user via the communication unit 151.

[0138] The determination process of starting driving 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 recognition process of the target user or perform beacon synchronization with the vehicle 12 to execute the determination process of starting driving. Also, for example, a measuring device integrated with an in-vehicle display device or the like of the vehicle 12 may execute the determination process of starting driving.

[0139] When the determination process is performed by the user terminal unit 11 or the vehicle 12, the determination result is included in the data transmitted from the user terminal unit 11 or the vehicle 12, and the state estimation unit 152 determines whether the target user has started driving based on the determination result.

[0140] If it is determined that the target 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 target user has started driving. As a result, the driving diagnosis, risk prediction, and damage prediction of the target user are appropriately performed, and based on the results, the adjustment of the detection parameters for dangerous driving and the feedback to the target user are performed. Also, the standard state pattern and the user collective state pattern of the target user are appropriately updated.

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

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

[0143] For example, based on the video data from the vehicle 12, the line of sight, blinking, facial expressions, etc. of the target user are detected and used for the estimation of the concentration, arousal level, fatigue level, emotions, etc. of the target user. Also, for example, based on the vehicle data from the vehicle 12, the content of the driving operation of the target user is estimated.

[0144] Furthermore, for example, based on the driving route of the vehicle 12 and the time zone when the target user is driving, the state of the target user is estimated. For example, if the heart rate and amount of movement of the target user increase rapidly and then the target user drives in a time zone different from normal (for example, late at night or early in the morning), or the vehicle 12 drives outside the daily living range of the target user, it is estimated that the target user is anxious due to some emergency.

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

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

[0147] The peripheral data acquisition unit 153 supplies the acquired peripheral 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 processes are started.

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

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

[0151] In addition, the detection of driving behavior may be performed by any one of the user terminal unit 11, the vehicle 12, and the server 13, or may be performed by the user terminal unit 11, the vehicle 12, and the server 13 sharing the work.

[0152] Next, the driving behavior detection unit 155 performs a dangerous driving detection process based on the detected driving behavior, the estimated state history of the target 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, or torque of the vehicle 12 based on OBD information or the like, or a sudden change in the acceleration or angular velocity detected by the user terminal unit 12.

[0154] For example, a weaving driving is detected by a periodic change in the speed, steering angle, or torque of the vehicle 12 based on OBD information or the like, or a periodic change in the acceleration or angular velocity detected by the user terminal unit 12.

[0155] For example, a shortage of the inter-vehicle distance is detected by the position of the preceding vehicle detected using a stereo camera, a lidar, or a millimeter-wave radar.

[0156] Here, the detection parameter adjusted in the above-described step S7 or the step S20 described later is used for the dangerous driving detection process. Therefore, as described above, the lower the driving suitability degree u of the target user, the lower the level of the dangerous driving to be detected, and the target is detected as dangerous driving from an earlier and milder stage.

[0157] The driving behavior detection unit 155 stores the detection result of the driving behavior in the storage unit 162 together with the detected time. Thereby, the driving behavior history of the target user is updated.

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

[0159] For example, when a decrease in the concentration or wakefulness of the target user occurs, or an increase in the fatigue level, stress level, or tension level of the target user occurs, the driving suitability degree u is decreased. On the other hand, for example, when an increase in the concentration or wakefulness of the target user occurs, or a decrease in the fatigue level, stress level, or tension level of the target user occurs, the driving suitability degree u is increased. Also, for example, when dangerous driving is detected, the driving suitability degree u is decreased according to the detection frequency. On the other hand, when the state where dangerous driving is not detected continues, the driving suitability degree u is increased according to the duration.

[0160] In addition, the driving suitability degree u may be corrected based on the quality of the driving operation of the target user, such as the smoothness of the way of stepping on the brake and accelerator pedals, the handling of the steering wheel during cornering, and the smoothness of acceleration and deceleration.

[0161] The diagnosis unit 154 stores the corrected driving suitability degree u and the estimated reduction factor in the storage unit 162 as the diagnosis result of the suitability of the target user's driving together with the diagnosis time. Thereby, the driving diagnosis history of the target user is updated.

[0162] In step S15, the risk prediction unit 156 performs risk prediction. Here, different from the process of step S5, the risk prediction unit 156 further uses the estimated state history and driving behavior history of the target user stored in the storage unit 162 in addition to the driving diagnosis history of the target user to perform risk prediction. For example, even if the driving suitability degree u of the target user is high, the higher the detection frequency of dangerous driving and the higher the degree of danger of the detected dangerous driving, the greater the predicted risk. On the other hand, for example, even if the driving suitability degree u of the target user is low, if no dangerous driving is detected, the predicted risk becomes smaller. In this way, since risk prediction is performed by further using the actual state and driving behavior of the target user during driving, the accuracy of risk prediction is improved compared with the process of step S5.

[0163] In addition, the risk prediction unit 156 can also predict not only the risk and its occurrence probability but also the time when the occurrence probability of the risk will increase in the future (hereinafter referred to as the risk increase time). For example, the risk prediction unit 156 estimates, based on the time-series changes in the concentration, tension, or arousal level of the target user, the time when they fall below a predetermined threshold as the risk increase time.

[0164] The risk prediction unit 156 supplies the prediction result of the risk 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, the factors causing the decrease in the driving suitability degree u and the detection results of dangerous driving), and the occurrence probability of the risk. Also, if necessary, it includes the risk increase time.

[0165] In step S16, similar to the process of step S6, damage prediction is performed. However, different from the process of step S6, the damage prediction unit 157 further uses the compliance degree of the target user to perform damage prediction. That is, the higher the compliance degree of the target user, the higher the possibility of avoiding risks, and the lower the compliance degree, the lower the possibility of avoiding risks. Therefore, the higher the compliance degree, the smaller the predicted damage, and the lower the compliance degree, the greater the predicted damage. The damage prediction unit 157 supplies the prediction results of the risk and the damage to the driving behavior detection unit 155 and the presentation control unit 158.

[0166] Here, depending on the compliance of the target user, the level of dangerous driving to be detected and the level of risk to be predicted change. For example, as described above, the greater the decrease in the compliance of the target 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 compliance, the lower the level of dangerous driving to be detected and the level of risk to be predicted.

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

[0168] In step S18, similar to the process of step S8, feedback to the target user is provided. Here, a specific example of the feedback information presented to the target user will be described.

[0169] For example, when it is estimated that the target user is sleep-deprived and sudden operation or weaving driving is detected, a warning about dangerous driving is given. For example, in the user terminal unit 11 or the vehicle 12 of the target user, an audio message such as "You are sleep-deprived. Please drive carefully." is output. For example, the part "Please drive carefully" becomes a risk avoidance plan, and the part "You are sleep-deprived" becomes the basis for presenting the risk avoidance plan.

[0170] For example, when it is estimated that the target user is outside the scope of daily life or is estimated to be feeling strong stress, if the approach to the vehicle ahead is detected, a warning against dangerous driving is issued. For example, in the user terminal unit 11 or the vehicle 12 of the target user, an audio message such as "Are you in a hurry? Let's drive calmly." is output. For example, the part "Let's drive calmly." becomes a risk avoidance plan, and the part "Are you in a hurry?" becomes the basis for presenting the risk avoidance plan.

[0171] For example, when it is estimated that the target user has engaged in intense exercise before driving, if the fixation of the target user's line of sight is detected, or if a large number of pedestrians are detected around the vehicle 12, a warning against the risk of dangerous driving is issued. For example, in the user terminal unit 11 or the vehicle 12 of the target user, an audio message such as "Are you sleepy? Please drive while paying attention to the surroundings." is output. For example, the part "Please drive while paying attention to the surroundings." becomes a risk avoidance plan, and the part "Are you sleepy?" becomes the basis for presenting the risk avoidance plan.

[0172] For example, when it is estimated that the target user is outside the scope of daily life or is estimated to be sleep-deprived, if a sudden operation or a weaving driving is detected while driving on a highway, a warning against dangerous driving is issued. For example, in the user terminal unit 11 or the vehicle 12 of the target user, an audio message such as "How about taking a break at the next service area? You were sleep-deprived yesterday and are currently driving in a weaving manner." is output. For example, the part "How about taking a break at the next service area?" becomes a risk avoidance plan, and the part "You were sleep-deprived yesterday and are currently driving in a weaving manner" becomes the basis for presenting the risk avoidance plan.

[0173] For example, when it is estimated that the target user is sleep - deprived or in an extremely tense state, and dangerous driving is frequently detected, a warning about an increase in the automobile insurance premium and an indication of its cause are given. For example, in the user terminal unit 11 or the vehicle 12 of the target user, an audio message such as "Dangerous driving due to lack of sleep (or tension) occurs frequently. If dangerous driving is detected x more times, you will lose the right to insurance cash - back." is output. For example, the part "If dangerous driving is detected x more times, you will lose the right to insurance cash - back." serves as a warning to the target user, and the part "Dangerous driving due to lack of sleep (or tension) occurs frequently." serves as the basis for giving the warning.

[0174] For example, when it is estimated that the target user is driving on an unfamiliar road outside the scope of daily life and the time of increased risk due to fatigue is estimated, an audio message such as "Are you tired? How about taking 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 target user's vehicle 12 before the time of increased risk. For example, the part "How about taking a break at Service Area A?" serves as a risk - avoidance plan, and the part "Are you tired?" serves as the basis for presenting the risk - avoidance plan.

[0175] Note that the risk - avoidance plan can be made more specific, for example, "Please reduce your speed to 60 km / h." or "Please face forward immediately." etc.

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

[0177] In step S19, the evaluation unit 159 evaluates the compliance of the user (target user). Specifically, the evaluation unit 159 acquires from the storage unit 162 the estimated state history and driving behavior history of the target user after feedback has been given to the target user. Then, based on the acquired history, the evaluation unit 159 detects the target user's reaction (for example, the content of driving) to the feedback.

[0178] In addition, based on the reaction of the target user, the evaluation unit 159 updates the evaluation value of the compliance of the target user. For example, if the feedback information presented this time includes a risk avoidance plan and the target user follows the risk avoidance plan, the compliance of the target user increases. Also, the shorter the time until the target user follows the risk avoidance plan (the faster the reaction speed), or the smaller the difference between the target user's reaction and the risk avoidance plan, the greater the increase in the compliance of the target user. Conversely, the longer the time until the target user follows the risk avoidance plan (the slower the reaction speed), or the greater the difference between the target user's reaction and the risk avoidance plan, the smaller the increase in the compliance of the target user.

[0179] On the other hand, if the target user does not follow the risk avoidance plan, for example, if the target user ignores the risk avoidance plan, or if the target user shows a reaction different from the risk avoidance plan, the compliance of the target user decreases. In particular, if the target user performs dangerous driving without following the risk avoidance plan, the decrease in the compliance of the target user is large.

[0180] The evaluation unit 159 supplies the updated compliance of the target user to the damage prediction unit 157 and stores it in the storage unit 162.

[0181] After that, the process proceeds to step S20.

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

[0183] In step S20, similar to the process of step S7, the detection parameters of the driving behavior are adjusted.

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

[0185] If it is determined that the target user has not stopped driving, the process returns to step S14. Thereafter, in step S21, the processes from step S14 to step S21 are repeatedly executed until it is determined that the target user has stopped driving. As a result, the driving diagnosis, risk prediction, and damage prediction of the target user are appropriately performed, and based on the results, the adjustment of the detection parameters for dangerous driving and the feedback to the target user are carried out. In addition, the compliance of the target user is appropriately updated.

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

[0187] In step S22, similar to the process in step S1, the estimation process of the state of the target user during non-driving is started.

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

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

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

[0191] For example, when safe driving is performed despite the driving suitability u being evaluated as low, the weights wx and wy in the formula (1) representing the above-described driving diagnosis model are set to be small. As a result, the driving suitability u of the target user will be evaluated higher than before. Conversely, when dangerous driving is frequently performed despite the driving suitability u being evaluated as high, the weights wx and wy in the formula (1) are set to be large. As a result, the driving suitability u of the target user will be evaluated lower than before.

[0192] Also, for example, when the difference between the most recent state pattern of the target user and the user set state pattern is large, and safe driving is performed despite the driving suitability u being evaluated as low, the weight wx in the above-described formula (1) is increased and the weight wy is decreased. That is, in driving diagnosis, the difference between the most recent state pattern of the target user and the standard state pattern is emphasized more.

[0193] For example, when the target user starts using the service, a model corresponding to an average user obtained through prior experiments or the like is used. Thereafter, through learning processing, each model is updated (personalized) to a model more suitable for the target user.

[0194] Note that this learning process does not necessarily have to be performed every time the driving of the target user ends. For example, it may be performed at any timing such as every predetermined period, every time the driving is performed a predetermined number of times, or every time the driving time increases by a predetermined time or more. Also, it is not necessarily required to perform the learning process for all models simultaneously, and the learning process may be performed at different timings for each model.

[0195] Further, for example, the learning unit 160 may perform learning of a fatigue degree estimation model that predicts the fatigue degree of the target user based on the transition of the fatigue degree of the target user before and during driving shown in the estimated state history. For example, the state estimation unit 152 can estimate the fatigue degree of the target user at each point on the planned driving route using the fatigue degree estimation model. Further, for example, the learning unit 160 can improve the estimation accuracy of the fatigue degree by performing learning of the fatigue degree estimation model in consideration of the surrounding state of the vehicle 12 such as the time zone, weather, traffic jam situation, road type (for example, general road or highway), etc.

[0196] Also, as a method of the learning process, for example, machine learning such as a neural network or any other method can be used.

[0197] In step S26, the insurance premium calculation unit 161 calculates the insurance premium of the user (target user). For example, when the insurance premium of the target user's automobile insurance or the cashback amount for the insurance premium fluctuates 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 compliance degree of the target user stored in the storage unit 162. The insurance premium calculation unit 161 stores the updated insurance premium or cashback amount of the target user in the storage unit 162.

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

[0199] Also, for example, the higher the frequency or cumulative number of dangerous driving, the higher the insurance premium (or the lower the cash-back amount), and the lower the frequency or cumulative number of dangerous driving, the lower the insurance premium (or the higher the cash-back amount). Further, for example, the higher the average or total predicted damage for dangerous driving, the higher the insurance premium (or the lower the cash-back amount), and the lower the average or total predicted damage for dangerous driving, the lower the insurance premium (or the higher the cash-back amount).

[0200] Also, for example, the lower the compliance level, the less the risk can be expected to decrease, so the insurance premium increases (or the cash-back amount decreases), and the higher the compliance level, the more the risk can be expected to decrease, so the insurance premium decreases (or the cash-back amount increases). In particular, when the target user drives dangerously without following the risk avoidance plan, for example, as a penalty, the increase range of the insurance premium (or the decrease range of the cash-back amount) becomes larger.

[0201] Note that this insurance premium calculation process does not necessarily have to be performed every time the driving of the target user ends. For example, it may be performed at any timing such as every predetermined period, every time a predetermined number of drives are performed, or every time the driving time increases by a predetermined time or more.

[0202] Also, this insurance premium calculation process may be executed, for example, at the time of the next insurance renewal to calculate the estimated amount of the insurance premium.

[0203] Furthermore, for example, in the case of automobile insurance per drive or per day, this insurance premium calculation process may be executed before driving to calculate the insurance premium. In this case, for example, the insurance premium is calculated using at least one of the driving suitability level u and the compliance level of the target user.

[0204] Also, the calculated insurance premium or cash-back amount may be presented to the target user as feedback information.

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

[0206] For example, the driving behavior is affected not only by the state of the user during driving but also by the state of the user before driving (e.g., actions, biological state, emotions, etc.). In contrast, as described above, by comprehensively considering the state of the user before driving in addition to the state of the user during driving and the driving behavior, the driving suitability degree u is more appropriately evaluated. Further, by improving the accuracy of the driving suitability degree u, the accuracy of risk prediction and damage prediction is improved. As a result, appropriate feedback information can be presented at a more appropriate timing, safety is improved, and the occurrence of accidents can be prevented.

[0207] In addition, since the basis of the result of the driving diagnosis or the basis for predicting the risk is shown in the feedback information, the user's acceptance is improved, and the probability that the user follows the risk avoidance plan increases. Also, when the presented basis is incorrect, for example, if the user instructs the server 13 to correct it and reflects it in the learning process, the accuracy of the driving diagnosis model and the risk prediction model is improved.

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

[0209] Also, by calculating the automobile insurance premium (including the cashback amount) based not only on the user's driving behavior but also on the driving suitability degree u and the compliance, a more appropriate insurance premium can be set for each user. As a result, for example, the user can be motivated to follow the risk avoidance plan, and the user can more surely avoid risks.

[0210] <<2. Modification Example>> Hereinafter, a modification example of the embodiment of the technology according to the present disclosure described above will be described.

[0211] <Modification Example Regarding System Configuration> The configuration example of the information processing system 10 in FIGS. 1 to 4 is an example, and it can be changed as necessary.

[0212] For example, in the above description, an example is 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). However, for example, it is also possible to distribute the processing 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 alone.

[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 part 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 result and the detection result 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 part 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 result and the prediction result to the server 13.

[0217] Further, for example, processing related to driving support may be performed by the user terminal unit 11. In this case, the user terminal unit 11 may be composed of a plurality of devices or may be composed of one device. Also, the user terminal unit 11 may acquire various data (for example, vehicle data, video data, audio data, etc.) from the vehicle 12 and use it for processing, or may not use the data from the vehicle 12 for processing. Also, a user collective state pattern may or may not be used in the driving diagnosis process. When using the user collective state pattern, for example, the server 13 performs learning of the user collective 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 collective 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 it, for example, an application program for performing the insurance premium calculation process is provided from the server 13. On the other hand, when the server 13 performs it, for example, data necessary for calculating the insurance premium is provided from the user terminal unit 11 to the server 13.

[0219] Furthermore, for example, among the processes related to driving support, almost all processes except the estimation process of the user's state during non-driving and the learning process of the standard state pattern may be performed by the vehicle 12 (in-vehicle system 101). In this case, for example, the user terminal unit 11 performs the estimation process of the user's state during non-driving and the learning process of the standard state pattern, and transmits data indicating the resulting estimated state history and standard state pattern to the vehicle 12 before driving. Then, the vehicle 12 executes the remaining processes. In this case, a user collective state pattern may or may not be used in the driving diagnosis process. When using the user collective state pattern, when using the user collective state pattern, for example, the server 13 performs learning of the user collective 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 collective state pattern to the user terminal unit 11 or the vehicle 12 of each user.

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

[0221] Also, for example, the processing may be shared between the user terminal unit 11 and the server 13. In this case, the data from the vehicle 12 may or may not be used for 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 necessary for the processing (such as the user's standard state pattern and the most recent state pattern, etc.) is provided from the user terminal unit 11 to the vehicle 12 and the server 13.

[0223] Also, for example, the processing may be shared among a plurality of servers. For example, the processing related to driving support and the processing related to insurance premium calculation may be performed by different servers.

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

[0225] Also, for example, the communication between the user terminal unit 11 and the server 13 may be performed via the vehicle 12. In this case, for example, the data from the user terminal unit 11 is first transmitted to the vehicle 12 and then transferred from the vehicle 12 to the server 13. Also, the data from the server 13 is first 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, the state data log before estimating the user's state may be directly used to perform the driving diagnosis of the diagnosis unit 154, the risk prediction of the risk prediction unit 156, and the detection of the driving behavior of the driving behavior detection unit 155. In this case, it is possible to delete the state estimation unit 152.

[0227] <Other Modification Examples> For example, in addition to the degree of deviation between the user's most recent state pattern and the standard state pattern or the user set average pattern, the current state of the user may be used to calculate the driving suitability degree u. For example, even if the difference between the user's most recent state pattern and the standard state pattern is small, if the user's arousal level is low, excited, or depressed, for example, there is a high possibility of dangerous driving. Therefore, for example, among the items indicating the user's state, for the items that have a great influence on driving, the current states of those items (for example, arousal level, excitement level, depression level) may be used to calculate the driving suitability degree u.

[0228] Also, the state of the user to be estimated is not limited to the above 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, the user's behavior that is usually assumed not to directly affect driving may be used to calculate the driving suitability degree u. For example, based on the user's purchase history, when the user shops in a different tendency than usual (for example, when making a very expensive purchase), it is assumed that the user's state (for example, emotion) is different from usual. Therefore, for example, the user's purchase history may be used to calculate the driving suitability degree u.

[0230] Further, for example, when calculating the driving suitability degree u, the ability of the user detected based on the state of the user when not driving, which is an ability that also affects driving, may be used. For example, the driving suitability degree u of a user with high judgment ability in daily life may be set high, and the driving suitability degree u of a user with low judgment ability may be set low.

[0231] In this case, for example, even if users with high and low judgment abilities repeat lane changes frequently in the same way, the risk predicted for the user with high judgment ability is low, and the risk predicted for the user with low judgment ability is high. However, even for a user with high judgment ability, if a risk avoidance plan is presented to reduce lane changes but the user ignores it and repeats lane changes, the compliance is evaluated as low, so the predicted risk is high.

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

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

[0234] Furthermore, for example, it is possible to use the estimated state history, driving diagnosis history, and driving behavior history 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 the accident based on the state and behavior of the user (driver) at the time of the accident, as well as the state of the user before driving.

[0235] Further, for example, the user terminal unit 11 or the vehicle 12 may be configured to obtain the driving suitability degree u of other users, the prediction results of risks, etc. from the user terminal unit 11 or the vehicle 12 of other users, or the server 13. Thereby, for example, when a dangerous vehicle driven by a user with a low driving suitability degree u is present nearby, for example, the user can avoid being involved in an accident caused by the dangerous vehicle by obtaining such information in advance.

[0236] Furthermore, the personalization of the state estimation model, the driving diagnosis model, the driving behavior detection model, and the risk prediction model is not necessarily required, and a model using a predetermined algorithm or the like may be used. Also, for example, instead of performing the learning of the above models for each user, the learning may be performed for the entire set of users, for example, and a common model may be used for each user.

[0237] Also, for example, based on the compliance degree of the user, the presentation method, the presentation frequency, and the presentation content of the feedback information may be changed.

[0238] <Application Example> The driving support process of the present technology can be applied not only to the vehicles exemplified above but also to various moving bodies such as motorcycles, bicycles, personal mobility devices, airplanes, ships, construction machines, and agricultural machines (tractors). That is, 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 the moving bodies to which the present technology can be applied include, in addition to those that move from place to place, moving bodies in which a part that performs work at a fixed location, such as some construction machines (e.g., fixed cranes), moves. Also, the moving bodies to which the present technology can be applied include, for example, moving bodies such as drones and robots that are remotely driven (operated) without a user boarding.

[0239] In addition to automobile insurance, the present technology can be applied to systems and devices that provide various types of insurance such as life insurance, property insurance, and medical insurance. For example, various insurance premiums (including cashback amounts) can be calculated based on the estimated state history and compliance 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 state estimation processing on the user and accumulates the estimated state history. Also, the user terminal unit 11 or the server 13 presents a proposal (risk avoidance plan) to the user to avoid risks such as illness based on the user's lifestyle and biological state based on the estimated state history, and evaluates the user's compliance based on the response. 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, biological state, and compliance, etc.

[0240] For example, the better the user's lifestyle and biological state, the lower the insurance premium, and the worse the user's lifestyle and biological state, the higher the insurance premium. Also, the higher the compliance of the user, the lower the insurance premium, and the lower the compliance of the user, the higher the insurance premium. Thus, by considering not only the user's lifestyle and biological state but also the user's compliance, an appropriate insurance premium can be set for each user in the same way as in the case of the automobile insurance described above.

[0241] <<3. Others>> <Configuration Example of Computer> The above-described series of processes can be executed by hardware or by software. When the series of processes are executed by software, the program constituting the software is installed in a computer. Here, the computer includes a computer incorporated in dedicated hardware, and, for example, a general-purpose personal computer that can execute various functions by installing various programs.

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

[0243] In a computer, a CPU (Central Processing Unit) 401, a ROM (Read Only Memory) 402, and a RAM (Random Access Memory) 403 are interconnected by 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, buttons, a microphone, an imaging device, 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 described above, the CPU 401 loads and executes, for example, a program recorded in the recording unit 408 via the input / output interface 405 and the bus 404 into the RAM 403, thereby performing the series of processes described above.

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

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

[0249] Note that the program executed by the computer may be a program whose processing is performed in time series according to the order described in this specification, or a program whose processing is performed in parallel or at a necessary timing such as when a call is made.

[0250] Also, in this specification, a system means a collection of a plurality of components (devices, modules (parts), etc.), and it does not matter whether all the components are in the same housing. Therefore, a plurality of devices housed in separate housings and connected via a network, and one device in which a plurality of modules are housed in one housing 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 take a configuration of cloud computing in which one function is shared and jointly processed by a plurality of devices via a network.

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

[0254] Furthermore, when a plurality of processes are included in one step, the plurality of processes included in that one step can be executed by one device or can be shared and executed by a plurality of devices.

[0255] <Example of composition combination> This technology can also adopt the following configuration.

[0256] (1) A diagnosis unit that diagnoses the suitability of the user's driving based on the state of the user before driving the moving body acquired in advance and the state of the user during driving acquired during driving, A presentation control unit that generates feedback information based on the diagnosis obtained by the diagnosis unit An information processing apparatus comprising: (2) Further comprising a driving behavior detection unit that detects the driving behavior, which is the behavior of the user or the moving body during driving, and the level of dangerous driving to be detected by the driving behavior detection unit changes based on the result of the diagnosis The information processing apparatus according to (1) above. (3) The diagnosis unit further performs the diagnosis based on the detection result of the driving behavior The information processing apparatus according to (2) above. (4) The diagnosis unit diagnoses the suitability of the user's driving before driving based on the state of the user before driving, and diagnoses the suitability of the user's driving during driving based on the state of the user during driving and the detection result of the driving behavior. The information processing apparatus according to (3) above. (5) Further comprising a risk prediction unit that predicts the risk related to the driving of the moving body by the user based on at least one of the result of the diagnosis and the detection result of the driving behavior The information processing apparatus according to any one of (2) to (4) above. (6) The feedback information includes the content of the risk and the basis on which the risk is predicted. The information processing apparatus according to (5) above. (7) The basis for predicting the risk is based on the result of the diagnosis. The information processing apparatus according to (6) above. (8) The feedback information includes a risk avoidance plan. The information processing apparatus according to any one of (5) to (7) above. (9) The lower the fitness level of the user's driving based on the diagnosis, the lower the level of dangerous driving to be detected by the driving behavior detection unit. The information processing apparatus according to any one of (2) to (8) above. (10) A learning unit that performs learning of the model for performing the diagnosis based on the result of the diagnosis and the detection result of the driving behavior. The information processing apparatus according to any one of (2) to (9) above, further comprising the above. (11) The diagnosis unit performs the diagnosis based on the difference between the user's most recent state and the user's standard state. The information processing apparatus according to any one of (1) to (10) above. (12) The diagnosis unit further performs the diagnosis based on the difference between the user's most recent state and the average state of users in a set of users including a plurality of users. The information processing apparatus according to (11) above. (13) A state estimation unit that estimates the state of the user before and during driving. Further comprising The diagnosis unit performs the diagnosis based on the estimation result of the user's state. The information processing apparatus according to any one of (1) to (12) above. (14) The state of the user includes at least one of the user's biological state, behavior, and emotion. The information processing apparatus according to (13) above. (15) The diagnosis unit performs the diagnosis based on the data indicating the state of the user acquired before and during the user's driving. The information processing apparatus according to any one of (1) to (12) above. (16) A diagnosis step of diagnosing the suitability of the user's driving based on the state of the user before driving a moving body acquired in advance and the state of the user during driving acquired during driving; A presentation control step of generating feedback information based on the diagnosis obtained by the diagnosis unit An information processing method including (17) A diagnosis step of diagnosing the suitability of the user's driving based on the state of the user before driving a moving body acquired in advance and the state of the user during driving 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

[0257] Note that the effects described in this specification are merely examples and are not limited, and there may be other effects.

Description of Signs

[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 Environment sensor, 64 Biosensor, 66 Output unit, 67 Control unit, 81 Biosensor, 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 Damage prediction unit, 158 Presentation control unit, 159 Evaluation unit, 160 Learning unit, 161 Insurance premium calculation unit

Claims

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 while driving the moving object that is acquired in advance; 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 claim 1 .

3. The diagnosing unit further performs the diagnosis based on the detection result of the driving behavior. The information processing device according to claim 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 claim 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 detection result of the driving behavior; The information processing device according to claim 2 .

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 claim 5 .

7. The risk is predicted based on the results of the diagnosis. The information processing device according to claim 6.

8. The feedback information includes a plan to avoid the risk. The information processing device according to claim 5 .

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 claim 2 .

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 claim 2 .

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 claim 1 .

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 claim 11.

13. A state estimation unit that estimates the state of the user before and during driving. In addition, The diagnosis unit performs the diagnosis based on the estimation result of the user's state. The information processing device according to claim 1 .

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 claim 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. The information processing device according to claim 1 .

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:

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