Safe driving support method, safe driving support system, and safe driving support server

Abstract

To detect a dangerous event using biometric data in a broad sense based on individual differences for each driver, and determine whether or not to issue an alarm.SOLUTION: In a method that a computer comprising a processor and a memory supports an operation of a vehicle, the computer generates an accident risk definition model which estimates a probability of a dangerous event as an accident risk by inputting first on-vehicle sensor data indicating a traveling state of the vehicle collected in the past and preset danger occurrence data; the computer generates a probability of occurrence of a dangerous event as accident risk estimation data by inputting second on-vehicle sensor data into an accident risk definition model; the computer generates an accident risk prediction model which predicts an accident risk after a predetermined time by inputting first biometric data of the driver when the second in-vehicle sensor data is collected and the accident risk estimation data; and the computer generates a biological condition estimation model for calculating a severity of the driver by inputting third biological data.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a safe operation support method and a safe operation support system that predict the risk of traffic accidents and support the safe operation of transportation facilities. [Background technology]

[0002] In recent years, the occurrence of traffic accidents caused by the health condition and fatigue of drivers of logistics trucks and long-distance buses has become a social issue. In order to prevent traffic accidents, technology that monitors the driver's condition while driving using biosensors and technology that monitors the behavior of the vehicle while driving are being applied.

[0003] For example, Patent Document 1 discloses a "safe driving support system that can continually learn the relationship between the driver's driving operations and the external environment, recognize deviations from the driver's usual internal state, and support safe driving." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2009-098970 A Summary of the Invention [Problem to be solved by the invention]

[0005] Patent document 1 provides a means for recognizing deviations from the driver's usual internal state by comparing the driving environment risks contained in the external environment recognized by sensing the external environment of a moving body with the driver's risk perception state estimated as an internal state from the driver's driving operation.

[0006] However, simply recognizing deviations from the driver's usual internal state (narrowly defined dangerous states) by performing state estimation on the relationship between the driver's driving operation and the external environment cannot detect states that require attention but are not apparent in the driving operation (broadly defined dangerous states).This has led to the problem that it is difficult to take measures to avoid broadly defined dangerous states in advance.

[0007] The present invention has been made in consideration of the above problems, and aims to detect dangerous conditions in a broad sense, taking into account individual differences between drivers, using biometric data, and to determine whether or not to issue an alert based on the results in order to detect dangerous conditions in a broad sense and avoid them in advance.

[0008] The present invention is a safe driving support method in which a computer having a processor and a memory supports driving of a vehicle, the method comprising: a first step in which the computer generates an accident risk definition model that estimates the probability of a hazardous event occurring as an accident risk using first on-board sensor data indicating a driving state of the vehicle collected in the past and preset hazard occurrence data as inputs; a second step in which the computer inputs second on-board sensor data indicating the driving state of the vehicle collected in the past to the accident risk definition model, estimates the probability of a hazardous event occurring, and generates accident risk estimation data; a third step in which the computer generates an accident risk prediction model that predicts an accident risk after a predetermined time using first biometric data calculated in advance from first biometric sensor data of the driver when the second on-board sensor data was collected and the accident risk estimation data as inputs; Using either a wearable device worn by the driver, a sensing device mounted on the vehicle, or an image recognition system that analyzes images of the driverThe method includes a fourth step of generating a biological state estimation model that uses third biological data collected in the past as input and calculates a person seriousness indicating to what extent second biological data calculated from second biological sensor data of the driver while driving deviates from the third biological data; a fifth step of the computer acquiring the second biological sensor data and calculating the second biological data from the second biological sensor data; a sixth step of the computer inputting the second biological data into the accident risk prediction model to predict the accident risk and inputting the second biological data into the biological state estimation model to calculate the person seriousness; and a seventh step of the computer determining whether or not to issue a warning based on the predicted accident risk and the calculated person seriousness. Effect of the Invention

[0009] Therefore, according to one aspect of the present invention, a dangerous state (hazardous event) in a broad sense can be detected based on the accident risk prediction model, the biological state estimation model, and the biological data. As a result, it is possible to determine whether or not to issue an alert based on the dangerous state in a broad sense, and further to present a countermeasure plan for avoiding the dangerous state in a broad sense.

[0010] The details of at least one implementation of the subject matter disclosed herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the disclosed subject matter will become apparent from the following disclosure, drawings, and claims. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram illustrating an example of a configuration of a safe driving support system according to a first embodiment of the present invention. [Diagram 2] FIG. 1 is a diagram illustrating an overview of processes performed in a safe driving support system according to a first embodiment of the present invention. [Diagram 3] 4 is a flowchart illustrating an example of a generation process of an accident risk prediction model performed by the safe driving support server according to the first embodiment of the present invention. [Figure 4] 5 is a flowchart illustrating an example of a biological state estimation model generation process performed by the safe driving support server according to the first embodiment of the present invention. [Diagram 5] 5 is a flowchart illustrating an example of a calculation process of biological data performed in the safe driving support server according to the first embodiment of the present invention. [Figure 6] 5 is a flowchart illustrating an example of a calculation process of biological data using heart rate data, which is performed in the safe driving support server according to the first embodiment of the present invention. [Figure 7] 5 is a flowchart illustrating an example of a risk prediction process performed by the safe driving support server according to the first embodiment of the present invention. [Figure 8] 5 is a flowchart illustrating an example of a warning presentation process performed by the safe driving support server according to the first embodiment of the present invention. [Figure 9A] FIG. 11 illustrates an example of a warning display screen according to the first embodiment of the present invention. [Figure 9B] FIG. 11 illustrates another example of the warning display screen according to the first embodiment of the present invention. [Figure 10A] FIG. 2 illustrates an example of a data structure of biometric data according to the first embodiment of the present invention. [Figure 10B] FIG. 2 illustrates an example of a data structure of the task and environment data according to the first embodiment of the present invention. [Figure 10C] FIG. 2 illustrates an example of a data structure of attribute information data according to the first embodiment of the present invention. [Figure 10D] FIG. 2 illustrates an example of a data structure of learning information data according to the first embodiment of the present invention. [Figure 10E] FIG. 2 illustrates an example of a data structure of risk occurrence data according to the first embodiment of the present invention. [Figure 10F] FIG. 2 is a diagram illustrating an example of a data structure of accident risk prediction data according to the first embodiment of the present invention. [Figure 10G] FIG. 11 illustrates an example of a data structure of person seriousness data according to the first embodiment of the present invention. [Figure 10H] FIG. 2 illustrates an example of a data structure of a joining table according to the first embodiment of the present invention. [Figure 10I] FIG. 11 illustrates an example of a data structure of alert definition data according to the first embodiment of the present invention. [Figure 10J] FIG. 2 illustrates an example of a data structure of countermeasure proposal data according to the first embodiment of the present invention. [Figure 11] FIG. 11 is a diagram illustrating an outline of a process performed in the safe driving support system according to the second embodiment of the present invention. [Figure 12] FIG. 11 illustrates an outline of a process performed in the safe driving support system according to the third embodiment of the present invention. [Figure 13] FIG. 13 is a diagram illustrating an example of a warning destination list screen provided to a driver manager according to the third embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. EXAMPLES

[0013] First, a first embodiment of the present invention will be described.

[0014] <System configuration> 1 is a block diagram showing an example of a main configuration of a safe driving support system according to a first embodiment of the present invention. The safe driving support system according to the present embodiment includes a safe driving support server 1 that supports the driving of one or more vehicles 7 via a network 14.

[0015] The vehicle 7 includes an on-board sensor 8 that detects driving conditions, a biosensor 12 that detects the driver's biometric data 64, a driver ID reader 11 that identifies the driver, a driving data collection device 10 that collects the detected on-board and biometric sensor data and the driver ID and transmits them to the safe driving support server 1, a prediction result notification device 9 that receives a warning from the safe driving support server 1 based on the driver's risk of a traffic accident (hereinafter referred to as accident risk) and presents it to the driver, and a work status input device 13 that receives input of the measurement status of the biometric data 64.

[0016] In the illustrated example, the driving data collecting device 10, the prediction result notifying device 9, the business condition input device 13, and the driver ID reading device 11 are independent devices, but they can be configured as one mobile terminal. In this case, the mobile terminal functions as a driving data collecting unit, a prediction result notifying unit, a business condition input unit, and a driver ID reading unit.

[0017] The on-board sensors 8 may include a Global Navigation Satellite System (GNSS) 81 that detects the vehicle's position information, an acceleration sensor 82 that detects the vehicle's behavior and speed, and a camera 83 that detects the driving environment as images.

[0018] The on-board sensor 8 is not limited to the above, and may be a distance measuring sensor that detects objects and / or distances around the vehicle, a steering angle sensor that detects driving operations, an angular velocity sensor that detects turning operations of the vehicle, etc. In addition, the acceleration sensor 82 is preferably a three-axis acceleration sensor.

[0019] The biosensor 12 includes a heartbeat sensor 121 that detects heartbeat data, and an acceleration sensor 122 that detects the movement of the driver. The heartbeat sensor 121 may be a sensor that detects the heartbeat based on an electrocardiogram, a pulse wave, or a heart sound.

[0020] The biosensor 12 is not limited to the above, and may be a sensor that detects the amount of sweat, body temperature, blinking, eye movement, electromyography, brain waves, etc. As the biosensor 12, a wearable device that can be worn by the driver, a sensing device attached to the inside of the vehicle such as a steering wheel, a seat, a seat belt, etc., or an image recognition system that captures the facial expression and behavior of the driver and analyzes the image may be used.

[0021] The driver ID reader 11 reads a card on which the driver's identifier is recorded. The driving data collection device 10 collects data from the in-vehicle sensor 8 and the biosensor 12 at a predetermined period and transmits the data to the safe driving support server 1 via the network 14.

[0022] In the illustrated example, the driver ID reader 11 is configured as a device that reads a card on which a driver's identifier is recorded, but may be configured differently. For example, when the driver ID reader 11 is configured as a mobile terminal and the mobile terminal is made to function as a driver ID reader, the driver ID may be read by having the driver input the driver's identifier, or the driver ID may be read by identifying the driver using a known face recognition technology using a camera of the mobile terminal.

[0023] The work status input device 13 receives information about the work status of the driver, such as driving, resting, or napping, and transmits the information to the safe driving support server 1.

[0024] The safe driving support server 1 is a computer including a processor 2, a memory 3, a storage device 4, an input / output device 5, and a communication device 6. The memory 3 loads each of the functional units, namely, a biological data calculation unit 31, an accident risk definition generation unit 32, an accident risk estimation unit 33, a biological state estimation model generation unit 34, an accident risk prediction model generation unit 35, a model selection unit 36, a danger prediction unit 37, a warning presentation unit 38, and a data collection unit 39, as a program. Each program is executed by the processor 2. The details of each functional unit will be described later.

[0025] The processor 2 operates as a functional unit that provides a specific function by executing processing according to the program of each functional unit. For example, the processor 2 functions as a biometric data calculation unit 31 by executing a biometric data calculation program. The same applies to other programs. Furthermore, the processor 2 also operates as a functional unit that provides each function of multiple processes executed by each program. A computer and a computer system are devices and systems that include these functional units.

[0026] The storage device 4 stores data used by each of the above-mentioned functional units. The storage device 4 stores the in-vehicle sensor data 61, the biosensor data 63, the task and environment data 74, the accident risk prediction model 71, the accident risk prediction data 66, the danger occurrence data 62, the biometric data 64, the accident risk definition model 70, the alert definition data 72, the accident risk estimation data 65, the countermeasure proposal data 73, the biometric state estimation model 69, the person seriousness data 67, the combination table 68, the attribute information data 75, and the learning information data 76.

[0027] As described below, the on-board sensor data 61 includes on-board sensor data 61-1 (first on-board sensor data) used when generating the accident risk definition model 70, and on-board sensor data 61-2 (second on-board sensor data) used when generating the accident risk prediction model 71. In the following description, when there is no need to distinguish between the on-board sensor data, the symbol "61" is used with the parts after "-" omitted.

[0028] As described below, the biometric data 64 includes biometric data 64-1 (first biometric data) used when generating an accident risk prediction model 71, biometric data 64-2 (second biometric data) used when inputting the accident risk prediction model 71 to generate accident risk prediction data 66, and biometric data 64-3 (third biometric data) used when generating a biological state estimation model 69. In the following description, when there is no need to distinguish between the biometric data, the symbol "64" is used with the parts after "-" omitted. Details of each data will be described later.

[0029] The input / output device 5 includes input devices such as a mouse, a keyboard, a touch panel, or a microphone, and output devices such as a display, a speaker, etc. The communication device 6 communicates with the vehicle via a network 14.

[0030] <Processing Overview> 2 is a diagram showing an overview of the processing performed by the safe driving support system. In the example shown in the figure, an example is shown that is configured with a learning phase that is performed before the vehicle 7 starts driving, and an operation phase that is performed while the vehicle 7 is driving.

[0031] First, the safe driving support system generates, in the safe driving support server 1, an accident risk prediction model 71 and a biological state estimation model 69 that are necessary for predicting a dangerous state of the driver while driving the vehicle 7.

[0032] First, the accident risk definition generating unit 32 inputs preset risk occurrence data 62 and previously collected vehicle-mounted sensor data 61-1 to generate an accident risk definition model 70 that estimates the probability of risk occurrence as an accident risk (S1). Note that the time series of the vehicle-mounted sensor data 61-1 and the time series of the risk occurrence data 62 are the same time series.

[0033] The accident risk definition model 70 estimates the accident risk, which is the probability of a dangerous event occurring, using on-board sensor data 61-2 (described later) indicating the past driving state of the vehicle as input. In the following, the value of the accident risk is expressed as a percentage.

[0034] The danger occurrence data 62 is data obtained by extracting events determined to be dangerous based on previously collected in-vehicle sensor data, and specifically, incidents such as sudden braking and sharp turns, or events linked to incidents, are determined by the driver or manager, or by a commercially available in-vehicle alarm device or AI, and the data is preset based on the type of the event and the date and time of occurrence. Furthermore, the danger occurrence data 62 may be set with a level of importance for each incident.

[0035] In this embodiment, an incident is defined as an event that leads to a traffic accident. An event that leads to an incident refers to a state in which the driver feels a sense of urgency or is startled. In addition, an example in which the danger increases as the importance level increases is shown. In the following, an incident or an event that leads to an incident is defined as a dangerous event.

[0036] Furthermore, the dangerous situation in the narrow sense described in the above problem includes the above incident, and the dangerous situation in the broad sense can include events that are linked to the above incident.

[0037] The accident risk definition model 70 is a machine learning model, and estimates the accident risk of a dangerous event by inputting the in-vehicle sensor data 61-2. For the machine learning model, a well-known or publicly known method such as a support vector machine, a neural network, or a logistic regression can be used. In addition, multiple accident risk definition models 70 may be generated for each type of dangerous event.

[0038] Next, the accident risk estimation unit 33 inputs the previously collected in-vehicle sensor data 61-2 into the accident risk definition model 70 to generate accident risk estimation data 65 that estimates the accident risk of a hazardous event occurring in time series (S2).

[0039] The accident risk estimation data 65 is data generated by expressing the probability of occurrence of a dangerous event for the in-vehicle sensor data 61-2 in the same time series as the in-vehicle sensor data 61-2.

[0040] Thereafter, the accident risk prediction model generation unit 35 inputs the accident risk estimation data 65 and the past biometric data 64-1, predicts and outputs an accident risk after a predetermined time from biometric data 64-2 (described later) of the driver of the vehicle 7 while traveling, and generates an accident risk prediction model 71 (S4). Note that the past biometric data 64-1 is calculated by the biometric data calculation unit 31 from the past biometric sensor data 63 corresponding to the in-vehicle sensor data 61-2 collected in the past.

[0041] In this embodiment, the biometric data 64-1 calculated from the biometric sensor data 63 collected in the past can be calculated using, for example, an average heart rate calculated from the driver's heart rate data, a maximum Lyapunov exponent calculated by nonlinear analysis of the heart rate data series, a frequency domain analysis of heart rate interval data extracted from the heart rate data, or an autonomic nerve function index (LF / HF, etc.), etc. The autonomic nerve function index (LF / HF, etc.) can be calculated by a method such as a time domain analysis or nonlinear analysis.

[0042] The biometric data 64-1 is a biometric index calculated by the biometric data calculation unit 31 from past biometric sensor data 63 corresponding to the time series of the in-vehicle sensor data 61-2.

[0043] The accident risk prediction model 71 is composed of a known or publicly known machine learning model, and inputs the biometric data 64-2 of the driver while driving, and outputs the accident risk after a predetermined time. The biometric data 64-2 of the driver while driving is a biometric indicator calculated by the biometric data calculation unit 31 from the driver's biosensor data 63, and is received by the safe driving support server 1 from the vehicle 7 while driving.

[0044] It is preferable to generate multiple models in advance as the accident risk prediction model 71 according to the type of accident risk, the type of biometric data used, the driving environment of the vehicle to which the prediction is applied, and the specified time. By generating multiple models in advance, it becomes possible to select and use the appropriate one in the risk prediction process.

[0045] In addition, multiple models may be generated based on attribute information data 75 that stores the driver's work characteristics (driving on public roads, driving on highways, working continuously day and night, etc.), driving experience (years of driving, driving skills, type of license held), and health characteristics (gender, amount of sleep, etc.).

[0046] Next, the biological state estimation model generation unit 34 generates a biological state estimation model 69 that estimates and outputs a degree of severity of the person indicating how much the input (current biological data 64-2) deviates from the previously measured biological data 64-3 by inputting the previously measured biological data 64-3 (S3). The degree of deviation of the current biological data 64-2 from the previously measured biological data 64-3 may be expressed as an abnormality degree.

[0047] The biological state estimation model 69 is composed of a known or publicly known statistical model or machine learning model. Typically, a plurality of biological state estimation models 69 are generated for each type of biological data 64-3, and are unsupervised models that input a certain type of biological data 64-3 and output statistics indicating the degree of abnormality (the person's seriousness) based on the biological data 64-3 used to generate the model.

[0048] Examples of statistical models that can be used include a statistical model that outputs a z-score obtained by normalizing the input using the average value and standard deviation of the biological data 64-3 used to generate the model, a statistical model that normalizes the output to the range of 0 to 1 using the maximum and minimum values ​​of the biological data 64-3 used to generate the model, a statistical model that outputs the rate of change from a value based on a statistical quantity of the biological data 64-3 used to generate the model, and a statistical model that outputs a quantile on the data distribution estimated from the biological data 64-3 used to generate the biological state estimation model 69.

[0049] In addition, as a machine learning model, for example, an anomaly detection model can be used that non-parametrically estimates a data distribution from the biological data 64-3 used to generate the biological state estimation model 69, and outputs the distance of that distribution from the cluster center.

[0050] In addition to the unsupervised biological state estimation model 69, a supervised biological state estimation model 69 can also be adopted. In this case, the biological state estimation model 69 can be configured using a well-known or known statistical model or machine learning model that estimates, as a target variable, learning information data 76 that collects the driver's drowsiness estimated by a well-known or known method, the number of blinks (number of blinks) that are an index of the driver's wakefulness, and the subjective fatigue level answered by the driver on a Visual Analogue Scale (VAS). In this case, for example, a regression model or the like can be adopted as the statistical model, and a support vector machine or a neural network can be adopted as the machine learning model.

[0051] The biometric data 64-3 input to the biometric state estimation model 69 is typically univariate, but may be multivariate. When the input is multivariate, it is desirable to input a combination of biometric data types that can be easily used to generate a countermeasure plan for avoiding a dangerous state in the warning presentation process described later. In this case, the biometric state estimation model 69 can be configured by a known method such as a multiple regression model, the Mahalanobis-Taguchi method, or multivariate statistical process management.

[0052] In addition, the biological state estimation model 69 may be generated for each driver, or a plurality of driver groups may be grouped together to generate one biological state estimation model 69. When grouping a plurality of driver groups, it is desirable to generate a biological state estimation model 69 for a group of drivers showing similar biological responses, since there are large individual differences in the biological body.

[0053] By generating a biological state estimation model 69 from a group of multiple drivers who show similar biological responses, it is possible to absorb individual differences in biological responses and shorten the time required to measure the data required to generate the biological state estimation model 69.

[0054] Furthermore, after the required amount of data is measured, it is desirable to switch from the biological state estimation model 69 generated from a group of multiple drivers showing similar biological responses to the biological state estimation model 69 generated only from the target driver. This makes it possible to output the individual severity taking into consideration the individual's biological responses.

[0055] Next, the safe driving support system uses the safe driving support server 1 to predict the probability of a dangerous event occurring to the driver while actually driving the vehicle 7, using the generated accident risk prediction model 71 and biological state estimation model 69.

[0056] First, the risk prediction unit 37 inputs biosensor data 63, such as heart rate data measured by the biosensor 12, received by the data collection unit 39 from the vehicle 7 to the biosensor calculation unit 31, and causes it to calculate biosensor data 64-2 (S5).

[0057] In addition, when there are multiple accident risk prediction models 71 or biological state estimation models 69, the risk prediction unit 37 selects the accident risk prediction model 71 or biological state estimation model 69 in accordance with the task and environment data 74 and attribute information data 75 in the model selection unit 36.

[0058] Thereafter, the risk prediction unit 37 inputs the biometric data 64-2 to the accident risk prediction model 71 to predict (generate) the accident risk prediction data 66 (S6A). The risk prediction unit 37 also inputs the biometric data 64-2 to the biological state estimation model 69 to calculate the person's seriousness data 67 for each biometric data type (S6B).

[0059] Then, the risk prediction unit 37 extracts the type (contribution index) of the biometric data 64-2 that contributed to the prediction of the accident risk prediction data 66, and matches the accident risk prediction data 66 with the accident risk prediction model 71 used to calculate the accident risk prediction data 66, the contribution index, and the individual severity data 67 of the contribution index, and stores them in the combination table 68 (S7A).

[0060] In extracting the contribution index, the risk prediction unit 37 extracts the contribution index from the biometric data 64-2, the accident risk prediction data 66, and the accident risk prediction model 71 used to calculate the accident risk prediction data 66.

[0061] For example, when the risk prediction unit 37 uses a linear model such as logistic regression as the accident risk prediction model 71, it can simply extract the biometric data type that maximizes the product of the regression coefficient and the biometric data 64-2 as the contributing index.

[0062] In addition, when a neural network or the like is used as the accident risk prediction model 71, the danger prediction unit 37 can calculate the contribution of each piece of biometric data 64-2 using, for example, SHapley Additive exPlanations (SHAP), a well-known method used to ensure the explainability of machine learning models, and extract the one with the maximum contribution as the contribution index.

[0063] Then, the warning presentation unit 38 judges whether to issue a warning (hereinafter, "alert judgment") based on the accident risk and the person's seriousness stored in the combination table 68 (S7B). If the warning presentation unit 38 judges that the driver is in a dangerous state in a broad sense, it generates a countermeasure plan for avoiding the dangerous state in a broad sense (S7C).

[0064] In the alarm determination, if the accident risk or the seriousness of the person exceeds a predetermined threshold, or if the values ​​related to the accident risk and the seriousness of the person exceed a predetermined threshold, the warning presentation unit 38 determines that the situation corresponds to a dangerous state (dangerous event) in the broad sense. Note that in the former case, the warning presentation unit 38 does not need to set a threshold for either the accident risk or the seriousness of the person when determining whether to issue an alarm.

[0065] For example, when the warning presentation unit 38 sets a threshold only for the accident risk, it can issue a warning for a scene that is generally determined to have a high accident risk. When the warning presentation unit 38 sets a threshold only for the person's seriousness, it can issue a warning for an abnormality in the biometric data 64-2, such as a sudden change in the driver's physical condition. In addition, thresholds may be set not only for the records of the corresponding accident risk and person's seriousness, but also for the time series of the accident risk and person's seriousness.

[0066] In generating countermeasures implemented by the warning presentation unit 38, countermeasures for avoiding a dangerous situation in a broad sense are generated based on the combination table 68 and the countermeasure data 73. From past knowledge, a method for physiologically interpreting a change in a certain type of biometric data 64-2 and a method for changing a certain type of biometric data 64-2 are known.

[0067] The countermeasure proposal data 73 stores physiological interpretations for when the value of each type of biometric data 64-2 increases or decreases, and the method of the biometric data 64-2 for increasing or decreasing the value. Since the contribution indexes that contributed to the prediction of the accident risk are stored in the combination table 68, it is possible to generate countermeasure proposals for avoiding dangerous situations in a broad sense according to the driver's condition by acquiring the physiological interpretations corresponding to the contribution indexes (described later) and the methods for increasing or decreasing the contribution indexes.

[0068] For example, when the contributing index is the autonomic nervous function index LF / HF and the LF / HF is in the positive direction and the severity of the individual is high, this can be physiologically interpreted as being under hypertension, and the warning presentation unit 38 may generate countermeasures to encourage relaxation, such as "try breathing slowly" in order to lower the driver's LF / HF.

[0069] In addition, when the contributing index is the average heart rate, the average heart rate is on a downward trend, and the person's seriousness is high, this can be physiologically interpreted as an increase in drowsiness, and the warning presentation unit 38 can generate a countermeasure to encourage the driver to relieve drowsiness, such as "Stop at the next convenience store, get out of the car and stretch."

[0070] In addition, when generating a countermeasure plan, the warning presentation unit 38 can add information based on the personal seriousness of the contribution index to the countermeasure plan. A warning based on the accident risk alone is not enough to convey the danger to the driver who receives the warning, but by presenting a warning in a form that the driver can understand based on the personal seriousness, it is expected that the driver's acceptance of the warning will be improved. In addition, when generating a countermeasure plan, the warning presentation unit 38 can add information based on the task and environment data 74 and the attribute information data 75 to the countermeasure plan.

[0071] After generating the countermeasure plan, the warning presentation unit 38 transmits a warning as an alert or a message including the countermeasure plan to the vehicle 7 determined to be in a broadly defined dangerous state (S8). The warning presentation unit 38 may transmit a warning not only to the vehicle 7 but also to the driver manager and display it on the display of the input / output device 5. In this case, the content to be displayed on the driver's display may include an image including the driver's position information, etc.

[0072] When the vehicle 7 receives the warning from the safe driving support server 1, the vehicle 7 outputs an alert from the prediction result notification device 9, and transmits the alert or a message to the driver.

[0073] The safe driving support server 1 determines a broadly defined dangerous state based on the accident risk after a specified time predicted using the accident risk prediction model 71 and the personal severity of the biometric data 64-2 estimated using the biometric state estimation model 69, thereby absorbing the individual differences between drivers contained in the biometric data 64-2 and detecting a broadly defined dangerous state.

[0074] Furthermore, when the safe driving support server 1 determines that a dangerous state exists in a broad sense, it generates a countermeasure plan for avoiding the dangerous state in a broad sense based on the contribution index and the personal seriousness of the contribution index, and transmits a warning including the countermeasure plan to the prediction result notification device of the vehicle in question or the display of the input / output device 5. In this way, the safe driving support server 1 can present a warning to the driver or the driver manager before the accident risk increases, and have them take measures to avoid the dangerous state in a broad sense.

[0075] As described above, the safe driving support server 1 can support the safe driving of each vehicle 7 by avoiding dangerous situations in the broad sense in advance and preventing the driver from falling into dangerous situations in the narrow sense.

[0076] <Processing details> Fig. 3 is a flowchart showing an example of a process for generating the accident risk prediction model 71. This process is performed before receiving the biometric data 64-2 from the vehicle 7 that performs hazard detection while traveling (the learning stage in Fig. 2), to generate the accident risk prediction model 71 in advance.

[0077] The accident risk definition generating unit 32 generates an accident risk definition model 70 by inputting previously collected risk occurrence data 62 and corresponding in-vehicle sensor data 61-1 (S1).

[0078] Next, the accident risk prediction model generation unit 35 inputs the previously collected in-vehicle sensor data 61-2 to the generated accident risk definition model 70 to generate accident risk estimation data 65 (S2).

[0079] Next, the accident risk prediction model generating unit 35 generates an accident risk prediction model 71 from the biological data 64-1 corresponding to the previously collected in-vehicle sensor data 61-2 and the accident risk estimation data 65 (S4). In this case, the accident risk prediction model generating unit 35 may generate an accident risk prediction model 71 for each similar task characteristic or environmental characteristic based on the task and environment data 74, and may add the information to the accident risk prediction model 71 as meta information.

[0080] Through the above process, the safe driving support server 1 generates an accident risk definition model 70 and generates accident risk estimation data 65, and then generates an accident risk prediction model 71 from the biological data 64-1 that corresponds chronologically to the accident risk estimation data 65.

[0081] In addition, the above processing is performed each time the amount or type of hazard occurrence data 62 increases by a certain amount, each time the accident risk estimation data 65 indicating a high accident risk increases to a certain degree, or at a certain frequency, and the accident risk definition model 70 and accident risk prediction model 71 are updated each time.

[0082] Fig. 4 is a flowchart showing an example of a process for generating a biological state estimation model 69. This process is performed before risk prediction is performed on a certain vehicle 7 while it is traveling (the learning stage in Fig. 2), and a biological state estimation model 69 is generated for each type of the biological data 64-3.

[0083] The biological state estimation model generation unit 34 first extracts a group of drivers showing similar biological responses (S11) as a group of drivers to be used in generating the biological state estimation model 69. Typically, using daily statistics such as the average, standard deviation, median, and quantile of a certain type of biological data 64-3, or weekly or monthly statistics, a group of drivers showing similar biological responses is extracted by performing clustering or the like using a known or publicly known method.

[0084] In this case, the biological state estimation model generation unit 34 may add, in addition to the biological data 64-3, information contained in the task and environment data 74, and information such as gender and age stratified based on age contained in the attribute information data 75, to the input for clustering.

[0085] Furthermore, when selecting the biometric data 64-3 to be input for clustering, the biometric state estimation model generation unit 34 may select only the biometric data 64-3 measured under the same measurement conditions based on the learning information data 76, and input only the biometric data 64-3 for the period stored in the learning information data 76. This is expected to control the measurement conditions and suppress the variation in the biometric data 64-3.

[0086] Furthermore, when a sufficient amount of biological data 64-3 is measured for a specific driver, the biological state estimation model generating unit 34 may extract only the specific driver instead of a group of multiple drivers. This makes it possible to estimate the severity of the specific individual that is more consistent with the characteristics of the specific individual.

[0087] Next, the biological state estimating model generating unit 34 receives the biological data 64-3 previously collected for the extracted driver group as an input, and generates a biological state estimating model 69 for each type of the biological data 64-3 (S3).

[0088] In this case, the biological state estimation model generation unit 34 may select only the biological data 64-3 measured under the same measurement conditions based on the learning information data 76, as in the above-mentioned driver extraction, and input only the biological data 64-3 for the period stored in the learning information data 76.

[0089] In addition, when the biological state estimation model generation unit 34 generates a supervised model as the biological state estimation model 69, the unit 34 receives as input the objective variable for the period stored in the learning information data 76 and the corresponding biological data 64-3, and generates the biological state estimation model 69 for each type of the biological data 64-3.

[0090] In this case, the biological state estimation model generation unit 34 may provide the biological state estimation model 69 with information on the type of the input biological data 64-3 and similar biological characteristics as meta-information.

[0091] After a group of drivers showing similar biological response characteristics is extracted by the above process, a biological state estimation model 69 is generated for each type of biological data 64-3 from the biological data 64-3 for the extracted group of drivers.

[0092] The above process of generating from a plurality of drivers is performed each time the number of target drivers increases by a certain amount, each time the biometric data 64-3 increases by a certain amount, or at regular intervals, to update the biological state estimation model 69. Furthermore, the above process of generating from a specific driver is performed each time the biometric data 64-3 increases by a certain amount, or at regular intervals, to update the biological state estimation model 69.

[0093] In addition, unlike the biometric data 64-1 used to generate the accident risk prediction model 71, the biometric data 64-3 used to generate the biometric state estimation model 69 does not need to correspond to the in-vehicle sensor data 61-2 or to cases where the risk of accidents is high, and there are fewer limitations on measurement scenes.

[0094] Therefore, since the biological data 64-3 used to generate the biological state estimation model 69 is easy to measure, the data can be measured in a shorter period of time than the generation of the accident risk prediction model 71, and it is expected that it will be possible to set a high update frequency.

[0095] Furthermore, since the occurrence of dangerous conditions in a broad sense is rare, it takes time to construct the accident risk prediction model 71 only from a group of multiple drivers with similar biological responses. On the other hand, in this embodiment, the individual differences in biological responses between multiple drivers are absorbed in the biological state estimation model 69, not the accident risk prediction model 71, so that the accident risk prediction model 71 can be generated using data of drivers with dissimilar biological responses. And the biological state estimation model 69 can be generated only from data of drivers with similar biological characteristics that can be easily collected while driving.

[0096] As described above, in comparison with a method for absorbing individual differences in biological responses in generating the accident risk prediction model 71, this embodiment can reduce the time required for generating a model required for implementing the danger prediction process.

[0097] 5 is a flowchart showing an example of a calculation process of the biometric data 64. This process is performed by the biometric data calculation unit 31 every time data is received from the vehicle 7.

[0098] The data collection unit 39 of the safe driving support server 1 receives data from the in-vehicle sensor 8 and the biosensor 12 from the driving data collection device 10 of the vehicle 7 (S21).

[0099] The data collection unit 39 stores the heartbeat data detected by the heartbeat sensor 121 and the acceleration data detected by the acceleration sensor 122 in the biosensor data 63 (S22). The data of the in-vehicle sensor 8 is stored in the in-vehicle sensor data 61.

[0100] Next, the biometric data calculation unit 31 calculates the biometric data 64 using the biometric sensor data 63 as input (S23) by a method described later in Fig. 6. The biometric data calculation unit 31 stores the calculated data in the biometric data 64 (S24). The biometric data 64 is calculated from the biometric sensor data 63 by the above process.

[0101] 6 is a flowchart showing an example of a calculation process using heart rate data for the biological data 64. This process is performed by the biological data calculation unit 31 every time the biological sensor data 63 is stored in the biological data 64.

[0102] The calculation process of the biometric data 64 using the heartbeat data is performed by a known or publicly known method, so only an outline of the process will be described below. First, the biometric data calculation unit 31 reads the heartbeat data from the biometric sensor data 63 (S31). The heartbeat data refers to data from which the interval between heartbeats obtained from the driver while driving by the heartbeat sensor 121 can be extracted, and specifically includes electrocardiogram data, pulse wave data, heart sound data, etc.

[0103] Next, the biological data calculation unit 31 extracts the beat interval IBI (Inter Beat Interval) from the heartbeat data and calculates its time series (S32). In the case of electrocardiogram data, the IBI may be calculated as an RRI (RR Interval) time series that uses the interval between electrocardiogram R waves. In the following, the RRI will be specifically mentioned as an example.

[0104] The biological data calculation unit 31 calculates the autonomic nerve function index by performing an analysis as required from the RRI. Examples of the analysis as required include a frequency domain analysis, a time domain analysis, and an RRI nonlinear domain analysis.

[0105] In the frequency domain analysis, the biological data calculation unit 31 calculates frequency domain indices from the RRI time series via power spectral density (S33). Since the RRI time series is irregularly spaced time series data, the power spectral density PSD (Power Spectral Density) is calculated by resampling at regular intervals using spline interpolation or the like, and then using an autoregressive model or maximum entropy method, or a known method such as the Lomb-Scargle method that can use irregularly spaced data.

[0106] The biological data calculation unit 31 calculates, from the calculated PSD, for example, the integral value LF of the low frequency region of 0.05 Hz-0.15 Hz, the integral value HF of the high frequency region of 0.15 Hz-0.40 Hz, TP which is the sum of LF and HF, LF / HF which is LF divided by HF, and LFnu which is LF divided by TP as a percentage, as frequency domain indices of autonomic nervous function indexes.

[0107] In the time domain analysis, the biological data calculation unit 31 calculates a time domain index by calculating statistics of the RRI time series and the ΔRRI time series, which is a difference series of adjacent RRIs (S34). For example, the average heart rate, which is the inverse of the average value of the RRI time series, and SDNN, which is the standard deviation of the RRI, are calculated. In addition, from the ΔRRI time series, for example, NN50, which is the total number of data whose absolute value of the difference value constituting the ΔRRI time series exceeds 50 ms, pNN50, which is NN50 divided by the total number of data of the ΔRRI time series, and SDSD, which is the standard deviation of the ΔRRI, are calculated and calculated as a time domain index of the autonomic nerve function index.

[0108] In the RRI nonlinear region analysis, the biological data calculation unit 31 calculates nonlinear features by various methods (S35). For example, the biological data calculation unit 31 calculates an ellipse area S obtained by approximating the plotted region to an ellipse through Poincaré plot analysis in which the RRI time series RRI(t) is plotted on the X-axis and the time series RRI(t+1) obtained by advancing the RRI time series by one time is plotted on the Y-axis. In addition, the biological data calculation unit 31 calculates similarity entropy, α1 and α2 by Detrended Fluctuation Analysis, and tone and entropy based on tone-entropy analysis, and calculates them as an RRI nonlinear region index of the autonomic nerve function index.

[0109] The biological data calculation unit 31 can also calculate a heartbeat nonlinear region index by performing a heartbeat nonlinear region analysis on the heartbeat data before the RRI extraction (S36). For example, the biological data calculation unit 31 applies chaos analysis to the heartbeat data to calculate a correlation dimension and a maximum Lyapunov exponent of the heartbeat data, and calculates them as a heartbeat nonlinear region index of the autonomic nerve function index.

[0110] Since the calculated autonomic nerve function index reflects the driver's biological state controlled by the autonomic nervous system, it is possible to realize the presentation of a warning according to the biological state by calculating an index for measuring the driver's biological state, which is necessary to present to the driver as a countermeasure plan for avoiding a dangerous state in a broad sense. For example, since LF / HF is an index for measuring the balance between sympathetic nerve activity, which is mainly enhanced in a stressed or tense state, and parasympathetic nerve activity, which is mainly enhanced in a relaxed state, it is possible to present a warning based on the driver's tense-relaxed state by having the biological data calculation unit 31 calculate LF / HF.

[0111] Thereafter, the biological data calculation unit 31 collectively stores the calculated autonomic nerve function indexes in the biological data 64 (S24). Through the above process, the autonomic nerve function index is calculated from the heart rate data in the biological sensor data 63, and stored as the biological data 64.

[0112] The biological data calculation unit 31 may execute the processes of steps S33 to S36 in parallel or sequentially.

[0113] 7 is a flowchart showing an example of a risk prediction process performed by the safe driving support server 1. This process is executed at a predetermined timing, such as every time the biometric data 64-2 is updated. The risk prediction unit 37 acquires the biometric data 64-2 from the latest period to a predetermined period (S12).

[0114] Next, the model selection unit 36 ​​identifies the driver ID and vehicle ID to be analyzed from the task and environment data 74, and selects the accident risk prediction model 71 and the biological state estimation model 69 based on the task and environment data 74 (S13 and S14). In selecting the accident risk prediction model 71, for example, a model to be used is selected based on the vehicle size, weather, traffic congestion, etc. of the task and environment data 74.

[0115] In selecting the biological state estimation model 69, for example, the model selection unit 36 ​​selects a model to be used based on information on the biological state estimation model 69 corresponding to the driver ID stored in the attribute information data 75.

[0116] Next, the risk prediction unit 37 inputs the biometric data 64-2 of the driver ID to be analyzed into the selected accident risk prediction model 71 to predict the accident risk, and stores the predicted accident risk in the accident risk prediction data 66 (S6A). Note that, although an example in which the biometric data 64-2 is input into the accident risk prediction model 71 has been shown above, the present invention is not limited to this, and task / environment data 74 and attribute information data 75 may also be added to the input of the accident risk prediction model 71.

[0117] For example, by inputting driving skill information obtained from driving aptitude tests included in attribute information data 75, personality and temperament information obtained from the Temperament and Character Inventory (TCI), Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire (TEMPS-A), Ten Item Personality Inventory for measuring the Big Five Personalities, and NEO-FFI, it is possible to take into account the driver's driving skills and personality and realize more accurate prediction of accident risk.

[0118] In addition, the risk prediction unit 37 inputs the biometric data 64-2 of the driver ID to be analyzed into the selected biological state estimation model 69, estimates a plurality of person seriousness for each biometric data type, and stores them in the person seriousness data 67 (S6B). Note that, although an example in which the biometric data 64-2 is input as a single variable has been shown above, the risk prediction unit 37 may input the multivariate biometric data 64-2 to calculate the person seriousness.

[0119] Next, the danger prediction unit 37 extracts contribution indexes that contributed to the prediction of the accident risk using the predicted accident risk prediction data 66, the accident risk prediction model 71 used to predict the accident risk, the biometric data 64-2 of the driver ID to be analyzed, and the estimated person seriousness data 67, and matches the contribution indexes, the person seriousness of the contribution index, the accident risk, and the accident risk prediction model 71 information, and stores them in the combination table 68 (S7A).

[0120] Through the above processing, at a predetermined timing, such as after the biometric data 64-2 is updated, the contribution index, the individual's seriousness of the contribution index, the accident risk, and accident risk prediction model information used to detect a broadly defined dangerous condition and issue a warning are stored in the combination table 68.

[0121] The danger prediction unit 37 may execute the processes of steps S13 and S14 in parallel or sequentially.

[0122] 8 is a flowchart showing an example of a warning presentation process performed by the safe driving support server 1. This process is executed at a predetermined timing, such as every time the connection table 68 is updated.

[0123] The warning presentation unit 38 reads an alert definition, which is a condition for presenting a warning, from the alert definition data 72 (S41). A plurality of alert definitions may be set according to the accident risk prediction model 71 and the biological state estimation model 69. In addition, a plurality of alert definitions may be set according to the target and method of presenting a warning, such as using a voice notification from the prediction result notification device 9 to the driver, displaying on a display, displaying on the display of the input / output device 5 of the driver manager, or notifying by email via the input / output device 5.

[0124] The warning presentation unit 38 refers to the accident risk and the person seriousness of each record in the combination table 68 (S42), and determines whether or not there is a driver whose accident risk exceeds the threshold value set in the warning definition (S7B).

[0125] When there is a driver who exceeds the threshold and is judged to be a driver who is to be ...

[0126] In addition, in order to increase the acceptability of the warning to the driver who receives the warning, the warning presentation unit 38 may add information to the countermeasure proposal that indicates how abnormal the state is for the driver by using the severity of the contribution index in the combination table 68. For example, the warning presentation unit 38 may add information such as "This state occurs only once in N years."

[0127] Furthermore, the warning presentation unit 38 can add information based on the task and environment data 74, the attribute information data 75, and the combination table 68 to the proposed countermeasures. For example, the warning presentation unit 38 may update the content of the feedback to be given as the proposed countermeasures based on information on the time N at which the accident risk is deemed to be high from the information on the accident risk prediction model 71 stored in the combination table 68. Also, for example, the warning presentation unit 38 may update the content of the feedback based on weather and temperature information stored in the task and environment data 74.

[0128] Then, the warning presentation unit 38 acquires the vehicle ID of the transmission target from the driver ID based on the task and environment data 74, and sets the vehicle 7 as the transmission target. When the transmission target is the driver manager rather than the driver, the warning presentation unit 38 may specify the manager ID instead of acquiring the vehicle ID, and set the input / output device 5 of the safe driving support server 1 operated by the manager as the transmission target.

[0129] Thereafter, the warning presentation unit 38 transmits a warning as an alert or a message to which an accident risk alert based on the warning definition that corresponds to the warning determination is added to the generated countermeasure plan (S8).

[0130] By the above process, a warning is sent to the vehicle of the driver who is detected as being in a dangerous state in the broad sense. In the vehicle 7 that receives the warning, the prediction result notification device 9 notifies the driver of the warning. In the safe driving support server 1, the warning presentation unit 38 displays the vehicle that sent the warning on the display of the input / output device 5.

[0131] 9A and 9B are diagrams showing an example of a warning presentation screen output by the prediction result notification device 9. The prediction result notification device 9 has a display (not shown), and displays a warning presentation screen 1000A when it receives a warning from the safe driving support server 1. The warning presentation screen 1000A includes an area 1010 that displays an accident risk alert, and a comment area 1020 that displays countermeasures for avoiding a dangerous state in a broad sense.

[0132] In the area 1010 that displays the accident risk alert, for example, in addition to a warning message 1011 about an increased accident risk, information 1012 based on the personal severity of the contributing indicator biometric data 64-2 can be displayed, so that the driver is not simply notified that there is a dangerous situation, but rather the warning can be presented in a form that is understandable to the driver based on the personal severity of the contributing indicator, such as "For you, this is a situation that occurs only once every six months," and this is expected to improve the driver's acceptance of the warning.

[0133] In addition, when displaying the information 1012 based on the individual's severity, the information may be displayed in a manner that makes it clear whether it is based on a biological state estimation model 69 generated only from the target driver's own biological data 64-2, or based on a biological state estimation model 69 generated from the biological data of multiple drivers who show biological response characteristics similar to that of the target driver.

[0134] For example, in the warning display screen 1000B of FIG. 9B, information 1012B based on the individual's severity in the area 1010 displaying the accident risk alert may be displayed in a form that makes it clear that the information is being compared with data from multiple drivers, such as "This is a condition that occurs only once every six months for drivers similar to you."

[0135] Also, for example, when a comparison is made with a plurality of drivers, information 1013 regarding which group of drivers is being compared may be displayed so that the comparison can be understood. Also, which biological state estimation model 69 is being used may be displayed by an icon or the like. As a result, it is expected that the driver will easily perceive the warning as something that concerns him or her.

[0136] Furthermore, by presenting, for example, an interpretation 1021 of the physiological state based on the contribution index and specific countermeasures 1022 for avoiding a dangerous situation in a broad sense in the comment area 1020 of the warning presentation screens 1000A and 1000B, the driver who receives the warning can understand and take the next action to resolve the dangerous situation, rather than just receiving the warning and leaving it at that.

[0137] In this example, the warning is presented by the warning display screen, but the warning may be presented by other methods. For example, the warning may be presented by mechanically reading out a text message having the same content as that displayed on the warning display screen.

[0138] <Data Structure> Next, we will show the characteristic structure of each piece of data used in the safe driving support system.

[0139] 10A shows an example of the data structure of the biometric data 64 held in the safe driving support system of the present invention. In addition to the driver ID 401, the vehicle ID 402, and the date and time 403, the biometric data 64 stores various autonomic nerve function indexes calculated by the biometric data calculation unit 31. Here, examples of the autonomic nerve function index include LF / HF 404, which is a frequency domain index, average heart rate 405, which is a time domain index, NN50 406, or α1 (407), which is an RRI nonlinear domain index, etc.

[0140] 10B shows an example of the data structure of the task and environment data 74 held in the safe driving support system of the present invention. The task and environment data 74 stores task information during the driver's work and driving environment information.

[0141] Typically, in addition to the driver ID 411, start date and time 412 and end date and time 413 indicating the duration of the information, and the vehicle ID 414 being used, vehicle type 415 indicating, for example, the size of the vehicle being driven, weather 416 and temperature 417 at a representative point in the driving area, task type 418 indicating the task status during the period from start date and time 412 to end date and time 413, and task schedule 419 such as the destination are stored.

[0142] 10C shows an example of the data structure of the attribute information data 75 held in the safe driving support system of the present invention. The attribute information data 75 stores basic information about the driver that does not change much over time.

[0143] Typically, in addition to a driver ID 421, a start date 422 and an end date 423 which are the duration period for which the basic information is applied, and a vehicle ID 424 of the vehicle being used, driving skill information measured by a driver aptitude diagnostic test or the like (e.g., skill A 425, pseudonym), personality temperament information measured by a Temperament and Character Inventory (TCI) or the like (e.g., personality A (426), pseudonym), gender 427, age 428, driving experience 429, etc. are stored. In addition to the above, a biological state estimation model ID 430, an accident risk prediction model ID 431, etc. are stored as various model information preferentially used for the driver.

[0144] 10D shows an example of the data structure of the learning information data 76 held in the safe driving support system of the present invention. The learning information data 76 stores information used when generating the biological state estimation model 69, and typically stores a driver ID 441, and a start time 442 and an end time 443, which are period information of the biological data 64 used for learning.

[0145] Furthermore, the learning information data 76 may store the measurement conditions 444 of the data of the period information in order to align the measurement conditions of the data used to generate the biological state estimation model 69. In this case, the driving data collecting device 10 may automatically determine and store the state from the in-vehicle sensor data 61-2 and the like (e.g., high-speed driving) as the measurement conditions 444, or the driver may manually input from among the conditions set via the work state input device 13 (e.g., lunch break).

[0146] When the biological state estimation model 69 is generated as a supervised model, in addition to the above, for example, a value 445 of subjective fatigue level measured by VAS may be stored as a response variable required for generating the supervised model.

[0147] 10E is an example of a data structure of danger occurrence data 62 held in the safe driving support system of the present invention. Data is stored in the danger occurrence data 62 that sets the type and occurrence date and time of a dangerous event detected by a driver, a driver manager, a commercially available vehicle alarm, or the like.

[0148] Typically, a vehicle ID 451, a date and time of occurrence of a dangerous event 452, and a type of danger 453 are stored. In addition, the importance of the event 454, which records the seriousness of the situation by visual confirmation or the like, may also be stored together with the detected dangerous event.

[0149] FIG. 10F shows an example of the data structure of the accident risk prediction data 66 held in the safe driving support system of the present invention.

[0150] The accident risk prediction data 66 stores an accident risk determined from the biometric data 64-2 using an accident risk prediction model 71. Typically, a driver ID 461, a date and time 462, an ID 463 of the accident risk prediction model 71 used to predict the accident risk, and an accident risk 464 indicating the occurrence probability (percentage) of a predicted hazardous event are stored.

[0151] FIG. 10G shows an example of the data structure of the person seriousness data 67 held in the safe driving support system of the present invention.

[0152] The person seriousness data 67 stores the person seriousness determined from the biometric data 64-2 using the biometric state estimation model 69. Typically, a driver ID 471, a date and time 472, a type 473 of the biometric data 64-2 from which the person seriousness was estimated, an ID 474 of the biometric state estimation model 69 used for the estimation, and the person seriousness 475 are stored.

[0153] In addition, since the biological state estimation model 69 is generated for each type of biological data, and the method of calculating the person's seriousness is expected to differ for each type of biological data, the output format of the person's seriousness is not necessarily constant.

[0154] For example, in the first record, type 473 of biometric data 64-2 outputs a statistic approximating a normal distribution for “average heart rate,” and indicates that the individual’s severity level 475 is a value of “+4SD” from the average using standard deviation (SD).

[0155] For example, the second record outputs the quantile of the data distribution for NN50, indicating that the individual severity level of 475 corresponds to the 99% quantile of the data set used to build the model.

[0156] 10H shows an example of the data structure of the combination table 68 held in the safe driving support system of the present invention. The combination table 68 is a table generated by matching values ​​used in the warning presentation process.

[0157] Typically, the driver ID 481, vehicle ID 482, date and time 483, accident risk prediction model ID 484 used for the prediction, predicted accident risk 485, contribution index 486 which is the type of biometric data that contributed to the accident risk prediction, biometric condition estimation model ID 487 used to estimate the biometric condition in relation to the contribution index, and the person's severity in relation to the contribution index 488 are stored.

[0158] Furthermore, when a certain record is determined to be in a dangerous state in the broad sense, a warning issued flag 489 that manages whether or not the warning issuing unit 38 has already issued a warning may be stored.

[0159] 10I is an example of a data structure of the alert definition data 72 held in the safe driving support system of the present invention. The alert definition data 72 stores a judgment condition used when judging whether or not a dangerous state in a broad sense applies.

[0160] Typically, an alert ID 491, an accident risk threshold 492, and a person seriousness threshold 493 are stored. In addition, for example, a contribution index condition 494 for limiting the contribution index when using the person seriousness threshold 493, a weather condition 495 for limiting the target weather during work, a person seriousness type 496 for limiting the judgment target so that only the person seriousness obtained in a specific output format is judged when making an alert judgment, and the like may be stored.

[0161] 10J shows an example of the data structure of countermeasure plan data 73 held in the safe driving support system of the present invention. The countermeasure plan data stores information used to generate a countermeasure plan for issuing a warning.

[0162] Typically, the countermeasure proposal ID 501, a contribution index 502 that is the condition for presenting the countermeasure proposal, a phenomenon 503 that indicates the state of the contribution index, an interpretation 504 of the driver's physiological state assumed from the phenomenon of the contribution index, and a countermeasure proposal 505 for avoiding the relevant broadly defined dangerous situation are stored.

[0163] Also, for example, a plurality of countermeasures 505 may be set for the same contribution index 502, phenomenon 503, and interpretation 504. In this case, a priority 506 for presenting the countermeasures 505 may be further stored.

[0164] As described above, the safe driving support system of this embodiment detects whether a broadly defined dangerous state exists based on the accident risk prediction data 66 calculated by inputting the biometric data 64-2 calculated by the safe driving support server 1 from the heart rate data of the biometric sensor 12 into the accident risk prediction model 71, and the person's seriousness data 67 calculated by inputting the biometric data 64-2 into the biometric state estimation model 69.

[0165] As a result, the safe driving support server 1 of this embodiment can absorb individual differences in the biometric data 64-2 and detect a dangerous state before it appears in the behavior of the vehicle 7. Furthermore, when a dangerous state in the broad sense is encountered, the safe driving support server 1 presents a warning including a countermeasure plan for avoiding the dangerous state in the broad sense based on the contribution index that contributed to the prediction of the accident risk, so that the driver who receives the warning can take the action required to avoid the dangerous state in the broad sense. As a result, the safe driving support server 1 can allow the driver of the vehicle 7 to avoid the dangerous state in the broad sense before it occurs.

[0166] In the above-mentioned first embodiment, the present invention is applied to a traffic support system that manages the vehicle 7, but the present invention is not limited to this. For example, instead of the vehicle 7, the present invention can be applied to a moving object that requires a driver or operator, such as a railroad car, a ship, or an aircraft. EXAMPLES

[0167] Next, a description will be given of a second embodiment of the present invention. Except for the differences described below, each part of the safe driving support system of the second embodiment has the same function as each part of the first embodiment to which the same reference numerals are attached, and therefore the description thereof will be omitted.

[0168] 11 is a diagram showing an outline of the process performed in the safe driving support system of the embodiment 2. In the embodiment 2 of the present invention, a point will be described where an accident risk is estimated by taking into consideration individual differences in biometric data.

[0169] The biological data 64-1 is input to the biological state estimation model 69 to calculate the person's seriousness data 67A (first person's seriousness) (S6C), and input to the accident risk prediction model generation unit 35.

[0170] The accident risk prediction model generating unit 35 receives as input the biometric data 64-1, the accident risk estimation data 65, and the person's seriousness data 67A to generate an accident risk prediction model 71 (S4).

[0171] Then, the risk prediction unit 37 inputs the biometric data 64-2 to the biometric state estimation model 69 to calculate the person's seriousness data 67B (second person's seriousness) (S6B), and outputs the person's seriousness data 67B to the accident risk estimation unit 33. The accident risk estimation unit 33 inputs the person's seriousness data 67A in addition to the biometric data 64-2 to the input of the accident risk prediction model 71 (S6A).

[0172] The process in step S7A in which the risk prediction unit 37 extracts the contribution index using the person's seriousness data 67B and generates the combined table 68 is the same as in the first embodiment.

[0173] Therefore, in the contribution index extraction and combination process performed by the risk prediction unit 37, the input that contributed to the accident risk estimation is selected from the biometric data 64-2 and the person's seriousness data 67B as the contribution index, and when the biometric data 64-2 is selected, the person's seriousness of the selected type of biometric data 64-2 is stored in the combination table 68, and when the person's seriousness data 67B is selected, the person's seriousness data 67B of the selected type is also stored in the combination table 68.

[0174] As a result, in Example 2, in addition to the judgment of whether to present a warning as described above, it is also possible to estimate the accident risk by taking into account individual differences in biometric data 64, thereby achieving the effect of making it possible to realize more accurate prediction of the accident risk. EXAMPLES

[0175] Next, a third embodiment of the present invention will be described. Except for the differences described below, each part of the safe driving support system of the third embodiment has the same function as each part of the first embodiment with the same reference numerals, and therefore the description thereof will be omitted.

[0176] Fig. 12 is a diagram showing an outline of the processing performed in the safe driving support system of the embodiment 3. In Fig. 12, the learning phase of the processing outline in Fig. 2 is omitted, and an outline of the processing is described with respect to the difference in the operation phase.

[0177] The safe driving support server 1 of the third embodiment is an example in which, instead of the warning presentation unit 38 presenting a warning each time after the danger prediction unit 37 adds a record to the combined table 68, the server 1 presents a list of targets for which warnings should be presented to the driver manager, allowing the driver manager to manually intervene to support the driver's safe driving.

[0178] Each time the risk prediction unit 37 of the safe driving support server 1 updates the combination table 68, or at regular intervals, the warning presentation unit 38 reads the combination table 68 and updates intervention candidates for which the driver manager may intervene (S15). For example, only records in the combination table 68 for which the report issued flag 489 is not "issued" and which are from the most recent N hours on the day of driving are extracted.

[0179] Thereafter, the warning presentation unit 38 performs a warning determination for the extracted record groups using the above-mentioned method, and extracts record groups that correspond to a broadly defined dangerous state (S7B). If there are record groups that correspond to a broadly defined dangerous state, the warning presentation unit 38 generates countermeasures for each of the record groups to avoid the broadly defined dangerous state (S7C).

[0180] The warning presentation unit 38 displays a list of warning destinations, which are a group of records for which countermeasures have been generated, on the display of the input / output device 5 (S16). In the list of warning destinations, warning destinations that are considered to require manual intervention by the driver manager are preferentially displayed at the top, allowing intervention by the driver manager.

[0181] For example, the warning presentation unit 38 can compare the task / environment data 74 with a group of records corresponding to dangerous conditions in a broad sense, and display at the top a list of records for which the driver cannot take countermeasures proposed for business reasons at his / her own discretion.

[0182] The input / output device 5 accepts intervention by the driver manager for the driver based on the list of warning destinations displayed on the display (S17). For example, if a countermeasure suggestion such as "Please take a rest at a nearby rest point" is displayed but there is no rest point nearby and the delivery time of the package is approaching, the driver manager can check the driver's condition by using an interface for voice communication with the driver displayed in the list of warning destinations.

[0183] If it is determined that the driver does not need to take a break immediately, the driver manager can provide the driver with a second-best solution by informing him of a place where he can take a break after completing a delivery.

[0184] In addition, for example, if a driver is repeatedly warned that he or she is "feeling increasingly drowsy and needs to rest," and the driver manager checks the driver's condition and determines that it will be difficult for the driver to carry out the original work schedule, the driver's workload can be adjusted by omitting some of the work scheduled for the latter half of the day's work schedule or first thing the next morning, allowing the driver to take a rest.

[0185] 13 is a diagram showing an example of a warning destination list screen 2000 output by the display of the input / output device 5. When the warning presentation unit 38 updates the extracted intervention candidates and generates a countermeasure plan, the input / output device 5 updates the warning destination list and displays the warning destination list screen 2000.

[0186] At the top of the warning destination list screen 2000, a current time 2001 and an administrator ID 2002 that is the ID of the driver administrator viewing the warning destination list screen 2000 are displayed. In the warning destination list 2003, a group of records for which countermeasures have been extracted and generated by the warning presentation unit 38 are displayed in a table format. By pressing a downward triangular button in the figure for each column, the group of records can be sorted by priority order according to the elements that the driver administrator wants to check.

[0187] Details of the driver corresponding to the record selected by the driver administrator for action are displayed in the lower area 2005. In the illustrated example, information on the driver ID "ZAX001" corresponding to record 2004 in the warning destination list 2003 is displayed.

[0188] In the area 2005, a current location 2011 showing the location of the driver on a map, a countermeasure plan 2012 related to the selected record 2004, a work schedule 2013 of the driver, a note 2014 showing the work status of the driver, and a driver contact button 2015 for directly communicating with the driver are displayed. In addition, the time transition of the biometric data 64 related to the contribution index on the day may be displayed together.

[0189] The current driver location 2011 shows the target driver's location 2021, the actual driving route 2022 (solid line) for that day, and the planned driving route 2022 (dotted line) on a map. In addition, the locations 2023 of other drivers around the driver are also displayed on the map.

[0190] The driver manager refers to the record 2004, the countermeasure plan 2012, the work schedule 2013, etc., and judges through the warning destination list screen 2000 whether the driver can execute countermeasures at his / her own discretion or whether it is necessary to directly check the driver's situation. Then, the driver manager can manually intervene to avoid a dangerous situation in a broad sense by pressing the driver contact button 2015 as necessary and directly talking to the driver.

[0191] As a result of the above, the driver manager can make the driver execute countermeasure plan 2012, which would be difficult for the driver to execute at his / her own discretion, or can make the driver execute a second-best countermeasure plan different from countermeasure plan 2012, thereby making it possible to avoid dangerous situations in a broad sense and to support the safe driving of the group of drivers under his / her management.

[0192] As described above, in the third embodiment, when a driver is in a dangerous state in a broad sense but the driver has difficulty in implementing a countermeasure plan at his / her own discretion, the driver is allowed to implement the countermeasure plan based on the driver's own condition, and the driver is enabled to take a more fundamental countermeasure plan to avoid the dangerous state in a broad sense. As a result, the safe driving support system has the effect of supporting the driver to take a more flexible countermeasure plan to avoid the dangerous state in a broad sense.

[0193] <Conclusion> As described above, the safe driving support server 1 according to the first to third embodiments can be configured as follows.

[0194] (1) A safe driving support method in which a computer (safe driving support server 1) having a processor (2) and a memory (3) supports driving of a vehicle (7), the safe driving support method comprising the steps of: (1) generating an accident risk definition model (70) for estimating the probability of a hazardous event occurring as an accident risk using first on-board sensor data (61-1) indicating a driving state of the vehicle (7) collected in the past and preset hazard occurrence data (62) as input; a second step of inputting second in-vehicle sensor data (61-2) indicating a state into the accident risk definition model (70) to estimate a probability of a hazardous event occurring and generate accident risk estimation data (65); and an accident risk prediction model in which the computer inputs first biometric data (64-1) calculated in advance from first biometric sensor data (63) of the driver when the second in-vehicle sensor data (61-2) was collected and the accident risk estimation data (65) to predict an accident risk after a predetermined time. a third step of generating a biological state estimation model (71) by the computer; a fourth step of generating a biological state estimation model (69) by inputting third biological data (64-3) calculated in advance and calculating a person's seriousness (person's seriousness data 67) by the computer; a fifth step of acquiring second biological sensor data (63) of the driver while driving from a biological sensor (12) of the vehicle (7) and calculating a biological state estimation model (64-2) indicating the state of the driver from the second biological sensor data (63); A safe driving support method comprising: a sixth step in which a computer inputs the biological state estimation model (64-2) into the accident risk prediction model (71) to predict the accident risk, and inputs the biological state estimation model (64-2) into the biological state estimation model (69) to calculate the person's seriousness (67); and a seventh step in which the computer determines whether or not to issue a warning based on the predicted accident risk (accident risk prediction data 66) and the calculated person's seriousness (67).

[0195] With the above configuration, the safe driving support server 1 can detect a dangerous state (hazardous event) in a broad sense based on the accident risk prediction model 71, the biological state estimation model 69, and the biological data 641.

[0196] (2) The safe driving support method according to (1) above, further comprising: an eighth step of extracting, when the computer determines that the warning should be issued, the accident risk prediction model (71), the accident risk (66), and a contribution index which is the type of biometric data that contributed to the prediction of the accident risk (66); and a ninth step of generating countermeasures to avoid a dangerous event based on the personal seriousness (67) of the contribution index by the computer.

[0197] With the above configuration, the safe driving support server 1 can detect dangerous conditions (dangerous events) in a broad sense based on the accident risk prediction model 71, the biological state estimation model 69, and the biological data 641, and when issuing an alert, can present countermeasures for avoiding the dangerous conditions in a broad sense.

[0198] (3) A safe driving support method as described in (2) above, characterized in that the safe driving support method further includes a tenth step in which the computer generates and outputs a warning including a countermeasure plan (countermeasure plan data 73) for avoiding the dangerous event.

[0199] With the above configuration, the safe driving support server 1 can detect dangerous conditions (dangerous events) in a broad sense based on the accident risk prediction model 71, the biological state estimation model 69, and the biological data 641, and when issuing an alert, can generate countermeasures to avoid the dangerous conditions in a broad sense and issue the alert.

[0200] (4) A safe driving support method as described in (1) above, characterized in that in the fourth step, the first biometric data (64-1) and learning information data (76) including a target period of at least the first biometric data (64-1) to be used for generating the biometric state estimation model (69) are input to generate the biometric state estimation model (69) for calculating the person's severity (67).

[0201] With the above configuration, when selecting the biometric data 64-3 to be input for clustering, the biometric state estimation model generation unit 34 may select only the biometric data 64-3 measured under the same measurement conditions based on the learning information data 76, and input only the biometric data 64-3 for the period stored in the learning information data 76. This is expected to control the measurement conditions and suppress the variation of the biometric data 64-3.

[0202] (5) A safe driving support method as described in (1) above, characterized in that in the sixth step, a combination table (68) is generated in which the accident risk (66), the contribution index, and the person's seriousness (67) regarding the contribution index are associated with each other and stored.

[0203] With the above configuration, at a predetermined timing, such as after the biometric data 64-2 is updated, the contribution index, the individual's seriousness of the contribution index, the accident risk, and accident risk prediction model information used to detect a broadly defined dangerous condition and issue a warning are stored in the combination table 68.

[0204] (6) A safe driving support method as described in (1) above, characterized in that in the tenth step, a warning including information based on the personal seriousness (67) and a countermeasure proposal for avoiding the dangerous event is displayed.

[0205] With the above configuration, in the area 1010 that displays the accident risk alert, in addition to a warning message 1011 about an increased accident risk, information 1012 based on the personal severity of the contributing indicator biometric data 64-2 can be displayed. This makes it possible to not only notify the driver that a dangerous situation is occurring, but also to present a warning in a form that the driver can understand based on the personal severity of the contributing indicator, such as "For you, this is a situation that occurs only once every six months," which is expected to improve the driver's acceptance of warnings.

[0206] (7) The safe driving support method according to (1) above, further comprising a twelfth step in which the computer inputs the first biometric data (64-1) into the biological state estimation model (69) to calculate a first person severity (67A), and in the third step, the first biometric data (64-1) calculated in advance from the first biometric sensor data (63) of the driver when the second in-vehicle sensor data (61-2) is collected, the accident risk estimation data (65), and the first person severity (67A) are input into the biological state estimation model (69) to calculate a first person severity (67A). a person seriousness (67) and a person in question, and generating an accident risk prediction model (71) for predicting an accident risk (66) after a predetermined time, and in the sixth step, inputting the biological state estimation model (64-2) into the biological state estimation model (69) to calculate a second person seriousness (67B), and inputting the biological state estimation model (64-2) and the second person seriousness (67B) into the accident risk prediction model (71) to predict the accident risk (66).

[0207] With the above configuration, the safe driving support server 1 can estimate the accident risk by taking into account individual differences in the biometric data 64, in addition to determining whether to issue a warning, thereby achieving the effect of making it possible to predict the accident risk with higher accuracy.

[0208] The present invention is not limited to the above-described embodiments, and includes various modified examples. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the configurations described. It is also possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0209] The above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. The above-mentioned configurations, functions, etc. may also be realized in software by the processor 2 interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a storage device such as the memory 3, a hard disk drive, or an SSD (Solid State Drive), or in a computer-readable non-transitory data storage medium such as an IC card, an SD card, or a DVD.

[0210] In addition, the drawings show control lines and information lines that are considered necessary for explaining the embodiments, but do not necessarily show all of the control lines and information lines included in an actual product to which the present invention is applied. In reality, it may be considered that almost all components are connected to each other. [Explanation of symbols]

[0211] 1. Traffic support server 2 processors 3. Memory 4. Storage Devices 7. Vehicles 8. Vehicle Sensors 12 Biometric sensors 31 Biometric data calculation unit 32 Accident risk definition generation unit 33 Accident Risk Estimation Department 34 Biological state estimation model generation unit 35 Accident risk prediction model generation unit 36 Model Selection Section 37 Risk Prediction Department 38 Warning display section 61 In-vehicle sensor data 62 Hazard Occurrence Data 63 Biometric sensor data 64 Biometric data 65 Accident risk estimation data 66 Accident risk prediction data 67 Personal Severity Data 68 Joined Tables 69 Biological State Estimation Model 70 Accident Risk Definition Model 71 Accident risk prediction model 72 Alert definition data 73 Countermeasures Proposal Data 74 Business and Environmental Data 75 Attribute information data 76 Learning Information Data

Claims

1. A safe driving support method for supporting driving of a vehicle by a computer having a processor and a memory, comprising: A first step in which the computer generates an accident risk definition model that estimates the probability of a hazardous event occurring as an accident risk using first on-board sensor data indicating a vehicle's traveling state collected in the past and preset hazard occurrence data as inputs; a second step in which the computer inputs second on-board sensor data indicating the vehicle's driving state, which has been collected in the past, into the accident risk definition model, and estimates the probability of a hazardous event occurring to generate accident risk estimation data; a third step in which the computer generates an accident risk prediction model that predicts an accident risk after a predetermined time period using as input first biometric data calculated in advance from first biometric sensor data of the driver when the second in-vehicle sensor data is collected and the accident risk estimation data; a fourth step in which the computer inputs third biometric data previously collected using any one of a wearable device worn by the driver, a sensing device mounted on the vehicle, and an image recognition system that analyzes an image of the driver, and generates a biological state estimation model that calculates a personal severity indicating how much second biometric data calculated from second biometric sensor data of the driver while driving deviates from the third biometric data; a fifth step of the computer acquiring the second biosensor data and calculating the second biodata from the second biosensor data; A sixth step in which the computer inputs the second biological data into the accident risk prediction model to predict the accident risk, and inputs the second biological data into the biological state estimation model to calculate the person's severity; A seventh step in which the computer determines whether or not to issue a warning based on the predicted accident risk and the calculated personal seriousness; A safe driving support method comprising:

2. The safe driving support method according to claim 1, an eighth step of extracting, when it is determined that the warning should be issued, the accident risk prediction model, the accident risk, and a contribution index which is a type of biometric data that contributed to the prediction of the accident risk; A ninth step in which the computer generates a countermeasure plan for avoiding the hazardous event based on the personal seriousness regarding the contribution index; The safe driving support method further comprises:

3. The safe driving support method according to claim 2, The safe driving support method further comprises a tenth step in which the computer generates and outputs a warning including the countermeasure proposal.

4. The safe driving support method according to claim 1, In the fourth step, A safe driving support method characterized by generating the biological condition estimation model that calculates the person's severity by inputting the first biological data and learning information data that includes a target period of at least the first biological data to be used for generating the biological condition estimation model.

5. The safe driving support method according to claim 2, In the sixth step, A safe driving support method, characterized by generating a combination table that associates and stores the accident risk, the contribution index, and the person's seriousness regarding the contribution index.

6. The safe driving support method according to claim 3, In the tenth step, A safe driving support method, characterized by displaying information based on the individual's seriousness and a warning including the proposed countermeasures.

7. The safe driving support method according to claim 1, The method further includes a twelfth step of inputting the first biological data into the biological state estimation model to calculate a first person severity level, In the third step, Generate the accident risk prediction model using the first biometric data, the accident risk estimation data, and the first person severity as inputs; In the sixth step, A safe driving support method characterized by inputting the second biometric data into the biometric condition estimation model to calculate a second person's seriousness, and inputting the second biometric data and the second person's seriousness into the accident risk prediction model to predict the accident risk.

8. a server having a processor and a memory; A driving assistance system that supports driving of a vehicle including a vehicle having an on-board sensor that detects a driving state and a biosensor that detects biometric data of a driver, The server, an accident risk definition generation unit that generates an accident risk definition model that estimates the probability of a hazardous event occurring as an accident risk using first on-board sensor data indicating a traveling state of the vehicle collected in the past and preset hazard occurrence data as input; an accident risk estimation unit that inputs second on-board sensor data that indicates a traveling state of the vehicle that has been collected in the past into the accident risk definition model, estimates a probability of a hazardous event occurring, and generates accident risk estimation data; an accident risk prediction model generation unit that generates an accident risk prediction model for predicting an accident risk after a predetermined time using as input first biological data calculated in advance from first biological sensor data of the driver when the second in-vehicle sensor data was collected and the accident risk estimation data; a biological state estimation unit that generates a biological state estimation model that receives third biological data previously collected using a wearable device worn by the driver, a sensing device mounted on the vehicle, or an image recognition system that analyzes an image of the driver, and calculates a personal severity level that indicates how much second biological data calculated from second biological sensor data of the driver while driving deviates from the third biological data; a biometric data calculation unit that acquires the second biometric sensor data and calculates the second biometric data from the second biometric sensor data; a danger prediction unit that inputs the second biological data into the accident risk prediction model to predict the accident risk, and also inputs the second biological data into the biological state estimation model to calculate the person's severity; a warning issuing unit that determines whether or not to issue a warning based on the predicted accident risk and the calculated personal seriousness; A safe driving support system comprising:

9. The safe driving support system according to claim 8, The warning presentation unit is A safe driving support system characterized by generating countermeasures to avoid dangerous events based on the accident risk prediction model, the accident risk, a contribution index which is a type of biometric data that contributed to the prediction of the accident risk, and the individual's seriousness regarding the contribution index when it is determined that the warning should be issued.

10. The safe driving support system according to claim 9, The warning presentation unit is A safe driving support system characterized by generating and outputting a warning including the countermeasure proposal.

11. The safe driving support system according to claim 8, The biological state estimation unit is A safe driving support system characterized by generating the biological condition estimation model that calculates the person's severity by inputting the first biological data and learning information data that includes a target period of at least the first biological data to be used for generating the biological condition estimation model.

12. The safe driving support system according to claim 9, The risk prediction unit is A safe driving support system, characterized by generating a combination table that associates and stores the accident risk, the contribution index, and the person's seriousness regarding the contribution index.

13. The safe driving support system according to claim 8, The risk prediction unit is A safe driving support system characterized by displaying information based on the individual's seriousness and a warning including proposed measures to avoid dangerous events.

14. The safe driving support system according to claim 8, The risk prediction unit inputs the first biological data into the biological state estimation model to calculate a first person seriousness, The accident risk prediction model generation unit Generate the accident risk prediction model using the first biometric data, the accident risk estimation data, and the first person severity as inputs; The risk prediction unit is A safe driving support system characterized by inputting the second biometric data into the biometric condition estimation model to calculate a second person's seriousness, and inputting the second biometric data and the second person's seriousness into the accident risk prediction model to predict the accident risk.

15. A vehicle operation support server having a processor and a memory, the server supporting a vehicle operation, an accident risk definition generation unit that generates an accident risk definition model that estimates the probability of a hazardous event occurring as an accident risk using first on-board sensor data indicating a traveling state of the vehicle collected in the past and preset hazard occurrence data as input; an accident risk estimation unit that inputs second on-board sensor data that indicates a traveling state of the vehicle that has been collected in the past into the accident risk definition model, estimates a probability of a hazardous event occurring, and generates accident risk estimation data; an accident risk prediction model generation unit that generates an accident risk prediction model for predicting an accident risk after a predetermined time period using as input first biological data calculated in advance from first biological sensor data of the driver when the second in-vehicle sensor data was collected and the accident risk estimation data; a biological state estimation unit that generates a biological state estimation model that receives third biological data previously collected using a wearable device worn by the driver, a sensing device mounted on the vehicle, or an image recognition system that analyzes an image of the driver, and calculates a personal severity level that indicates how much second biological data calculated from second biological sensor data of the driver while driving deviates from the third biological data; a biometric data calculation unit that acquires the second biometric sensor data and calculates the second biometric data from the second biometric sensor data; a danger prediction unit that inputs the second biological data into the accident risk prediction model to predict the accident risk, and also inputs the second biological data into the biological state estimation model to calculate the person's severity; a warning issuing unit that determines whether or not to issue a warning based on the predicted accident risk and the calculated personal seriousness; A safe driving support server comprising:

16. The safe driving support server according to claim 15, The warning presentation unit is A safe driving support server characterized in that, when it is determined that the warning should be issued, a countermeasure plan for avoiding a dangerous event is generated based on the accident risk prediction model, the accident risk, a contribution index which is a type of biometric data that contributed to the prediction of the accident risk, and the personal seriousness regarding the contribution index.

17. The safe driving support server according to claim 16, The warning presentation unit is A safe driving support server that generates and outputs a warning including the countermeasure proposal.

18. The safe driving support server according to claim 15, The biological state estimation unit is A safe driving support server characterized by generating the biological condition estimation model that calculates the person's severity by inputting the first biological data and learning information data that includes a target period of at least the first biological data to be used for generating the biological condition estimation model.

19. The safe driving support server according to claim 16, The risk prediction unit is A safe driving support server that generates a combination table that associates and stores the accident risk, the contribution index, and the person's seriousness regarding the contribution index.

20. The safe driving support server according to claim 15, The risk prediction unit is A safe driving support server that displays information based on the individual's seriousness and a warning including a proposed countermeasure for avoiding a dangerous event.

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