Information processing apparatus, information processing method, and program

Abstract

To appropriately assist driving of a user from a viewpoint including a status of a user who drives or an environment in which a vehicle travels.SOLUTION: A control section acquires a running characteristic that is obtained on the basis of a user characteristic associated with driving at time when a user drives a vehicle, a vehicle characteristic of the vehicle, and data of the environment traveled by the vehicle. The control section acquires a travel history at time when the vehicle or another vehicle runs with a past running characteristic, when the acquired running characteristic corresponds to or is similar to the past running characteristic at time when the vehicle or the other vehicle ran in the past within an acceptable limit. The present control section executes drive assistance on a travel route where the vehicle runs, on the basis of the acquired travel history.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] BACKGROUND ART Conventionally, navigation devices have been proposed that provide recommended route guidance based on vehicle information such as vehicle width and road information (road width, etc.) (for example, Patent Document 1 listed below). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-157760 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-described conventional technology does not take into account circumstances other than the vehicle information or road information. That is, even when driving the same vehicle type on the same road, the road may or may not be recommended depending on the driving user's situation or the driving environment. For example, the user's driving characteristics, including the user's driving skill or mental characteristics, may affect the determination of the recommended road. Furthermore, conditions such as weather conditions and the time of day may also affect the determination of the recommended road. Therefore, for users with insufficient skills, road sections suitable for users with sufficient skills may not be wide enough, or may require a lot of effort to navigate through those road sections. Furthermore, users with insufficient skills may experience problems such as excessive driving fatigue due to anxiety, tension, etc., on road sections suitable for users with sufficient skills.

[0005] Therefore, an object of the disclosed embodiments is to appropriately assist a user in driving from a perspective that includes the driving situation of the user or the driving environment. [Means for solving the problem]

[0006] One aspect of the disclosed embodiment is exemplified by an information processing device including a control unit. The control unit acquires driving characteristics determined based on user characteristics related to driving when a user drives a vehicle, vehicle characteristics of the vehicle, and environmental data in which the vehicle travels. Then, when the acquired driving characteristics match or are similar to past driving characteristics of this vehicle or another vehicle within an acceptable limit, the control unit acquires driving history of when this vehicle or another vehicle traveled using the past driving characteristics. Then, the control unit provides driving assistance on a driving route traveled by the vehicle based on the acquired driving history. [Effects of the Invention]

[0007] The control unit acquires driving characteristics determined based on user characteristics related to driving when the user drives the vehicle, vehicle characteristics of the vehicle, and environmental data in which the vehicle travels. The control unit then acquires past driving history for the user, vehicle, and environment that matches or is similar to the driving characteristics. Therefore, the control unit can perform driving assistance, such as route guidance or advice during driving, based on the driving history based on past user characteristics, vehicle characteristics, and environmental data that match the user, vehicle, and environmental data currently being targeted for driving assistance. In other words, the control unit can appropriately assist the user's driving from a perspective that includes the driving situation of the user and the driving environment. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a driving assistance system. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of the in-vehicle device. [Figure 3] FIG. 3 is a diagram illustrating the traveling characteristics. [Figure 4] FIG. 4 is a diagram illustrating the configuration of the traveling characteristics database. [Figure 5]FIG. 5 is a diagram illustrating an example of the configuration of the driving history database. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of the road characteristics database. [Figure 7] FIG. 7 is a flowchart illustrating the route calculation A. [Figure 8] FIG. 8 is a flowchart illustrating the route calculation B. [Figure 9] FIG. 9 is a flowchart illustrating the cautionary section setting process. [Figure 10] FIG. 10 is a flowchart illustrating a driving advice process performed by the in-vehicle device. [Figure 11] FIG. 11 is a flowchart illustrating an example of information collection processing by an in-vehicle device. [Figure 12] FIG. 12 is a flowchart illustrating a skill determination process performed by the support server. [Figure 13] FIG. 13 is a flowchart illustrating a mental stability calculation process performed by the support server. [Figure 14] FIG. 14 is a flowchart illustrating the processing of the support server according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] <Embodiment 1> A driving assistance system 50 according to one embodiment will be described below with reference to the drawings.

[0010] (composition) FIG. 1 is a diagram illustrating an example of the configuration of a driving assistance system 50. This driving assistance system includes an assistance server 2 and on-board devices 10-1, 10-2, etc., mounted on vehicles 1-1, 1-2, etc., that receive assistance from the assistance server 2. FIG. 1 also illustrates a weather server 3. The assistance server 2 is an example of an information processing device, and executes an information processing method according to a program.

[0011] When vehicles 1-1, 1-2, etc. are referred to collectively, they are simply referred to as vehicle 1. When in-vehicle devices 10-1, 10-2, etc. are referred to collectively, they are simply referred to as in-vehicle devices 10. In addition, the number of vehicles 1 in the driving assistance system 50 is not limited to two. In addition, the number of in-vehicle devices 10 in the driving assistance system 50 is not limited to two. Furthermore, the driving assistance system 50 may include a terminal device carried by the driver of the vehicle 1 and linked to the in-vehicle device 10. The terminal device is, for example, a smartphone.

[0012] As shown in FIG. 1, the in-vehicle device 10, the support server 2, and the weather server 3 are connected to one another via a network N1. The network N1 is, for example, a Long Term Evolution (LTE) network. This includes wireless networks such as 5G, 6G, and wireless local area networks (LANs), as well as wired public networks such as the Internet.

[0013] The in-vehicle device 10 cooperates with the assistance server 2 and receives assistance from the assistance server 2 to provide various information to the driver of the vehicle 1 while the vehicle 1 is traveling. The in-vehicle device 10 includes a car navigation system. The in-vehicle device 10 may also cooperate with the assistance server 2 to provide the driver of the vehicle 1 with the functions of the car navigation system. The driver may also carry a terminal device. The terminal device may receive assistance from the assistance server 2 together with the in-vehicle device 10 or instead of the in-vehicle device 10 to provide driving assistance to the driver.

[0014] The assistance server 2 cooperates with the in-vehicle device 10 of the vehicle 1 to provide driving assistance to the vehicle 1. Here, the driving assistance includes, for example, route guidance to the destination of the vehicle 1, driving advice (also called guidance) to the driver of the vehicle 1, etc. The assistance server 2 may provide driving assistance to the vehicle 1 in cooperation with a terminal device that cooperates with the in-vehicle device 10, or a terminal device carried by the user instead of the in-vehicle device 10.

[0015] The support server 2 collects vehicle driving data, driving history data, vital data of the driver while driving, facial image data, etc. of the vehicle 1, along with vehicle characteristic data of the vehicle 1, from multiple in-vehicle devices 10. The support server 2 stores the vehicle conditions when each vehicle 1 is driving in a database as vehicle driving data. The vehicle driving data includes, for example, information identifying the driver, the date and time of departure, the road section traveled, the date and time the driving ended, the speed of the vehicle 1, the steering angle, the accelerator opening (or the angle of change from the accelerator pedal stopped state), the angle of the brake pedal, images from an in-vehicle camera or a drive recorder, sounds detected by a microphone, etc.

[0016] The assistance server 2 evaluates the driving skill of the driver of the vehicle 1 based on the vehicle driving data of the vehicle 1. For example, the assistance server 2 counts the frequency of sudden acceleration and sudden braking based on the time change in driving speed. The assistance server 2 also evaluates the number of sudden turns and the stability of the steering angle while driving based on the time change in steering angle data. The assistance server 2 may also evaluate driving stability based on, for example, images of the road taken by an in-vehicle camera or images taken by a drive recorder. The assistance server 2 may also evaluate the number of turns based on, for example, data on the change in steering angle just before stopping or parking and the distance traveled forward or backward by the vehicle 1. The assistance server 2 evaluates the driving skill of the driver at that time based on such vehicle driving data of the vehicle 1 while the driver is driving.

[0017] The support server 2 also evaluates the driver's mental stability based on vital data, facial image data, etc. of the driver while the vehicle 1 is traveling. Examples of vital data include pulse rate, blood pressure, and respiratory rate. The support server 2 compares the driver's pulse rate, blood pressure, respiratory rate, etc. while the vehicle 1 is traveling with the pulse rate, blood pressure, respiratory rate, etc. of the driver when the vehicle 1 is stopped before the vehicle 1 starts. The support server 2 then evaluates the driver's tendency to become nervous, whether or not the driver has confidence in driving, etc., based on, for example, the degree of change in the driver's pulse rate, blood pressure, respiratory rate, etc. before and after the vehicle starts, and the degree of change in these vital data while traveling. The support server 2 may also recognize, for example, changes in facial expression before and after the driver starts and changes in facial expression while traveling from facial images of the driver captured by an in-vehicle camera, and evaluate the driver's tendency to become nervous, whether or not the driver has confidence in driving, etc.

[0018] The assistance server 2 then stores the evaluated driving skill, mental stability, etc. in a database as user characteristic data. The assistance server 2 may evaluate the driving skill, mental stability, etc. for each driver periodically or each time the driver drives, and store the evaluation results together with information identifying the driver as user characteristic data. Therefore, the assistance server 2 holds the latest user characteristic data of the driver when the vehicle 1 is traveling.

[0019] Furthermore, the assistance server 2 stores in a database vehicle characteristic data of the vehicle 1. The vehicle characteristic data of the vehicle 1 is data that may affect the ease of driving the vehicle 1. The vehicle characteristic data of the vehicle 1 includes, for example, the size of the vehicle, the presence or absence of a camera (the presence or absence of a view-around monitor function, a rear monitor function, etc.), the type of tires (whether or not winter tires are installed), etc.

[0020] The support server 2 also acquires weather data for the area where the vehicle 1 is traveling from the weather server 3 via the network N1. The weather data includes, for example, weather (sunny, rainy, cloudy, snowy, foggy, etc.), amount of rainfall, amount of snowfall, temperature, wind speed, etc. Then, the support server 2 acquires, for example, weather data for the area where the vehicle 1 is traveling from the weather server 3. Weather data is stored in association with the road section traveled or the area including the road section.

[0021] The weather server 3 is, for example, a server that provides AMeDAS data from the Japan Meteorological Agency or a server that provides weather data from a weather forecasting company. The support server 2 accesses the weather server 3 via, for example, an Application Programming Interface (API) to monitor the vehicle 1. Weather data for the road section being driven or the area including the road section can be obtained.

[0022] In this embodiment, the weather data obtained from the weather server 3 is called environmental data. However, environmental data is not limited to weather data. The environmental data may include, for example, information about the time period in which the vehicle 1 travels. The time period information may be defined by a start time and an end time, or may include information distinguishing between morning, noon, night, evening, etc.

[0023] In this way, the assistance server 2 stores information identifying the driver, the travel date and time, vehicle travel data, the driver's user characteristic data, the vehicle characteristic data of the vehicle 1, and environmental data for a plurality of vehicles 1. The assistance server then determines overall characteristics when the vehicle 1 is traveling for a combination of the driver's user characteristic data, the vehicle characteristic data, and the environmental data, and stores the overall characteristics in a database.

[0024] In this embodiment, the overall characteristics can be considered as a driving capability determined by the driver, the vehicle, and environmental data, and are called driving characteristics. Furthermore, in this embodiment, the assistance server 2 stores past driving history data of the driver, the vehicle 1, and the environment in which the vehicle 1 has driven, each having its own driving characteristics. The driving history data includes, for example, information on the date and time when the vehicle 1 has driven, information on the road section traveled, and obstacle events indicating the presence or absence of obstacles, the type of obstacle, etc.

[0025] When the assistance server 2 receives a request for driving assistance from a vehicle 1-1, it acquires information identifying the driver D1 who drives the vehicle 1-1 and information identifying the vehicle 1-1. The assistance server 2 then obtains a driving characteristic A from the user characteristic data of the driver D1 who drives the vehicle 1-1, the vehicle characteristic data of the vehicle 1-1, and environmental data of the area where the vehicle is scheduled to travel. The assistance server 2 then obtains past driving characteristics that match or are similar to the driving characteristic A from the database, and obtains driving history data in which the vehicle 1 traveled with the past driving characteristics. The past driving characteristics may be the driving characteristics of the driver D1 or the driving characteristics of a driver other than the driver D1. The past driving characteristics may also be the driving characteristics of the vehicle 1-1 or the driving characteristics of a vehicle other than the vehicle 1-1.

[0026] The assistance server 2 then determines a driving route by excluding road sections that had problems in the driving history data for past driving characteristics that match or are similar to the driving characteristics A. The assistance server 2 may also determine a driving route by prioritizing road sections that had no problems in the driving history data for past driving characteristics and on which the vehicle 1 was able to drive smoothly. The assistance server 2 assists the driver D1 in driving the vehicle 1-1 in the environment of the environmental data, based on information on the determined driving route, the road sections that had problems, or the road sections that were able to be driven smoothly.

[0027] 2 is a diagram illustrating an example of the hardware configuration of the in-vehicle device 10. The hardware configuration of the in-vehicle device 10 is similar to that of a normal computer. The in-vehicle device 10 has a CPU 11, a main memory unit 12, and external devices connected via an interface (I / F), and executes information processing by a program.

[0028] The CPU 11 executes a computer program that has been loaded in an executable manner into the main storage unit 12, and provides the functions of the in-vehicle device 10. The main storage unit 12 is also called simply a memory, and stores the computer program executed by the CPU 11, data processed by the CPU 11, etc. The CPU 11 is also called a processor. However, the CPU 11 is not limited to a single processor. Alternatively, the CPU 11 may be a single processor connected via a single socket and have a multi-core configuration.

[0029] Furthermore, the CPU 11 may include a plurality of different types of processors. For example, at least a part of the processing of the in-vehicle device 10 may be performed by a Digital Signal Processor (DSP), a Graphics Processing Unit (GPS), or the like. The graphics processing unit (GPU), dedicated processors such as numerical calculation processors, vector processors, image processing processors, and Application Specific Integrated Circuits (ASICs) are used. At least a part of the in-vehicle device 10 may be a dedicated large scale integration (LSI) such as a field-programmable gate array (FPGA) or other digital circuit. Furthermore, at least a part of the in-vehicle device 10 may include an analog circuit.

[0030] The main memory unit 12 is a memory device that includes a dynamic random access memory (DRAM) and a static random access memory (SRAM). Memory (SRAM), Read Only Memory (ROM), etc. The main memory unit 12 can be called a control unit.

[0031] Examples of the external devices include an external storage unit 13, a display unit 14, an operation unit 15, a communication unit 16, a vital sign sensor 17, an interior camera 18A, and an exterior camera 18B. The external devices do not have to be integrated with the control unit (CPU 11, main storage unit 12, etc.). Each of the external devices may be connected to the housing of the in-vehicle device 10 via an interface.

[0032] The external storage unit 13 is used, for example, as a storage area that supplements the main storage unit 12, and stores computer programs executed by the CPU 11, data processed by the CPU 11, etc. The external storage unit 13 is a hard disk drive, a solid state drive (SSD), etc. Furthermore, a drive device for a removable storage medium may be provided in the in-vehicle device 10. The removable storage medium may be, for example, a Blu-ray disc, a Digital Versatile Disc (DVD), a Compact Disc (CD), a flash memory card, or the like.

[0033] The display unit 14 is, for example, a liquid crystal display, an electroluminescence panel, an organic light emitting diode (OLED), etc. The operation unit 15 is, for example, an array of push buttons, a keyboard, a pointing device, etc. In this embodiment, a touch panel is exemplified as a pointing device. The communication unit 16 exchanges data with other devices on the network. The communication unit 16 is compatible with Long Term Evolution (LTE), 4th Generation Mobile Communication System(4G),5th Generation Mobile Communication System(5G) ), or a wireless communication device that accesses a wireless network such as a wireless LAN.

[0034] Vital sensor 17 acquires vital data such as pulse rate, blood pressure, respiratory rate, etc. Vital sensor 17 may acquire vital data by contacting the subject, or may acquire vital data without contacting the subject.

[0035] The contact-type vital sensor 17 may have, for example, a light-emitting diode (LED) that emits infrared light and a light-receiving unit, and measures the pulse rate as well as the blood oxygen concentration from changes in the infrared light that passes through the subject's finger or the like. The contact-type vital sensor 17 may also have, for example, an LED that emits light within a specific range, such as visible light, and a light-receiving unit. The contact-type vital sensor 17 irradiates the subject's skin (such as the wrist) with visible light, and the light-receiving unit receives the light reflected from the lower part of the skin. The contact-type vital sensor 17 then calculates the blood flow through blood vessels near the skin surface from the received light and measures the pulse rate, etc. Such an optical vital sensor 17 may also estimate blood pressure based on changes in blood flow. The contact-type vital sensor 17 may also have a pressure sensor and measure blood pressure directly from blood vessels in the finger or wrist, for example. The contact-type vital sensor 17 may be in the form of a ring or a wristband, or may be embedded in the steering wheel of the vehicle 1.

[0036] Non-contact vital sensor 17 may have, for example, an antenna for emitting and receiving electromagnetic waves. This non-contact vital sensor 17 may, for example, irradiate the subject with microwaves, millimeter waves, or the like, and measure the respiratory rate from the movement of the body surface, and measure the pulse rate from the blood flow in the blood vessels (e.g., veins) on the body surface. Non-contact vital sensor 17 may also have, for example, a camera and measure the respiratory rate from the movement of the subject's upper body. Such non-contact vital sensor 17 may also measure the pulse rate and respiratory rate from the movement of the subject's facial surface, for example.

[0037] The CPU 11 collects vital data from the driver of the vehicle 1 using the vital sensor 17, and stores the vital data in the main memory unit 12, external memory unit 13, etc., along with information that identifies the driver, information on the date and time of driving, and information on the road section being driven.

[0038] The interior camera 18A captures an image of the driver's face. The exterior camera 18B captures images of the surroundings of the vehicle (front, rear, left and right). The exterior camera 18B may be a camera that captures an image behind the vehicle. For example, the CPU 11 combines the images from the exterior camera 18B and outputs a view-around image of the surroundings of the vehicle to the display unit 14. For example, the CPU 11 may output a back monitor image of the area behind the vehicle captured by the exterior camera 18B.

[0039] 1 also have the same configuration as the CPU 11, main memory 12, and external devices connected via an interface (I / F) of the in-vehicle device 10. However, the support server 2 and weather server 3 are general computers, and may not include, for example, the vital sensor 17, the in-vehicle camera 18A, the out-of-vehicle camera 18B, etc. The support server 2 and weather server 3 are not limited to a single computer, but may be constructed on a cloud or virtual computer consisting of a collection of multiple computers.

[0040] 3 is a diagram illustrating the off-road characteristics in this embodiment. As already mentioned, the off-road characteristics are an index showing the driving ability determined by the user characteristic data of the driver, the vehicle characteristic data, and the environmental data.

[0041] The user characteristic data may include, for example, the driving skill and mental stability of the user as a driver, but is not limited to the driving skill and mental stability.

[0042] The vehicle characteristic data also includes, for example, the size of the vehicle 1, the presence or absence of an on-board camera or the type of on-board camera, the type of monitor function of the on-board camera (view-around function, rear monitor function, etc.), and the type of tires (winter, summer, etc.), but is not limited to these.

[0043] On the other hand, the environmental data includes weather data of the road section traveled by the user as the driver in the vehicle 1, the time period during which the vehicle was passed, etc. Here, the weather data is, for example, weather, amount of rainfall, amount of snowfall, temperature, wind speed, etc. Furthermore, the time period during which the vehicle was passed is, for example, morning, noon, evening, night, etc. However, the environmental data is not limited to these.

[0044] FIG. 4 illustrates an example of the configuration of the driving characteristics database in which the support server 2 manages driving characteristics data. In FIG. 4, the driving characteristics database is illustrated in a tabular format, with each row (each record) representing an individual piece of driving characteristics data. However, the structure of the driving characteristics database is not limited to a tabular database. For example, the driving characteristics database may define and manage driving characteristics data using a combination of keywords and values.

[0045] In the example of Figure 4, each record (driving characteristic data) has the following major items: NO, user characteristic data The user characteristic data includes driving skill and mental stability as sub-items (sub-items). The vehicle characteristic data includes size, camera, and tires as sub-items. The environmental data includes weather, rainfall, snowfall, temperature, wind speed, and traffic time period as sub-items.

[0046] The NO is identification information that uniquely identifies each record. The assistance server 2 can identify the combination of user characteristic data, vehicle characteristic data, and environmental data by the NO. In addition, in FIG. 4, each record with a NO from 1 to 10 corresponds to, for example, the record of user U1. Furthermore, each record with a NO from 11 to 20 corresponds to, for example, the record of user U2. However, the number of records in the driving characteristics database for one user (driver) is not limited to 10. Furthermore, each record (driving characteristics data) may include information that identifies the user who is the driver.

[0047] As already described in FIG. 1, the driving skill indicates the driving ability of the user who is the driver. The driving skill may be, for example, a numerical value ranging from 0 to 100. Here, the numerical value exemplifies the degree of contribution to the driving characteristics. Therefore, a user with the best driving skill is assigned a numerical value of 100, and a user with the worst driving skill is assigned a numerical value of 0.

[0048] Furthermore, mental stability indicates the inner stability of the user who is the driver. For example, mental stability is a numerical value ranging from 0 to 10. For mental stability, the numerical value also exemplifies the degree of contribution to driving characteristics. Therefore, a user with the highest mental stability is assigned a numerical value of 10, and a user with the lowest mental stability is assigned a numerical value of 0. The reason why the numerical range of mental stability is different from the numerical range of driving skill is because it is assumed that the influence of driving skill on driving characteristics is greater than the influence of mental stability. However, the numerical range of mental stability may be set to the same range as the numerical range of driving skill based on experience, experimental values, etc.

[0049] Other characteristic data are set in the same manner. That is, for each sub-item, a high score is assigned to a sub-item that contributes more to the off-road characteristics, and a low score is assigned to a sub-item that contributes less to the off-road characteristics. For example, in the vehicle characteristic data, 0, 50, and 100 points are assigned to large, medium, and small vehicle sizes. Regarding the camera, if the view-around monitor function is present, the contribution is 100 points; if the rearview monitor function is present, the contribution is 70 points; and if the exterior camera 18B is not present, the contribution is 0 points.

[0050] For example, the contribution of tires is linked to the amount of snowfall, and when there is snowfall, winter tires are assigned 100 points and summer tires are assigned 0 points. On the other hand, the contribution of tires may also be linked to, for example, the temperature. For example, when the temperature is below 3 degrees Celsius, winter tires are assigned 100 points and summer tires are assigned 0 points. On the other hand, when the temperature is 3 degrees Celsius or higher, both winter tires and summer tires are assigned 0 points.

[0051] Of the environmental data, the weather is set, for example, 50 points for sunny weather, 40 points for cloudy weather, 30 points for rain or fog, and 20 points for snow. The amount of rainfall is set to 0 points for a heavy rain warning and 50 points for no warning. The amount of snowfall is set, for example, 0 points for 10 cm or more of snow, 5 points for less than 10 cm of snow with snow accumulation, and 50 points for no snow accumulation. The temperature is set, for example, 0 points for temperatures below 3 degrees Celsius and 50 points for temperatures above 3 degrees Celsius. The wind speed is set, for example, 0 points for a strong wind warning and 50 points for no warning. The passing time zone is set to 0 points if it is night, 5 points if it is evening, and 10 points if it is morning or daytime.

[0052] The above-mentioned set values ​​can be determined empirically or experimentally. For example, a vehicle driving simulation game is executed with a combination of the user and the vehicle 1 of the user characteristic data and the vehicle characteristic data, and a correlation is generated between the tendency of the driving characteristics and the performance of the vehicle driving simulation game. In the case of a vehicle driving simulation game, the sub-items of the environmental data do not need to be used.

[0053] On the other hand, the values ​​of the sub-items of the environmental data may be set based on the actual occurrence rate of obstacles for a combination of user characteristic data, vehicle characteristic data, and environmental data. For example, under the same conditions for all sub-items other than weather, the scores (contributions) of sunny, cloudy, rainy, foggy, and snowy weather may be determined so that the occurrence rate of obstacles or the occurrence rate of traffic accidents when the weather is sunny, cloudy, rainy, foggy, and snowy are inversely correlated with the driving characteristics. The same applies to other sub-items.

[0054] The off-road characteristic is a comprehensive evaluation value of the setting values ​​of the above sub-items. The comprehensive evaluation is performed, for example, by simply adding the values ​​of all the sub-items, normalizing the result to a range of 0 to 10, and converting it to an integer. Therefore, the off-road characteristic is, for example, an integer ranging from 0 to 10. However, the range of the off-road characteristic value is not limited to the range of 0 to 10. The off-road characteristic value may also include decimals. Furthermore, the comprehensive evaluation method is not limited to simple addition. For example, if the travel time is at night or there is a lot of snow, the setting values ​​of the sub-items may be weighted so that their contribution to mental stability is higher, and then the comprehensive evaluation may be performed.

[0055] FIG. 5 is a diagram illustrating the configuration of a driving history database. Each record (driving history data) in the driving history database has elements such as number, date and time, road section, and obstacle event. The structure of the driving history database is not limited to a table format. For example, the driving history database may be configured with keywords and values.

[0056] NO is the same as in Figure 4, and is identification information that uniquely identifies each record in the driving characteristics database of Figure 4 and the driving history database of Figure 5. Therefore, the two databases of Figure 4 and Figure 5 are linked by the value of NO. In other words, each driving history data in Figure 5 corresponds to the driving history at the time of each driving characteristic in Figure 4 associated with NO.

[0057] The date and time are, for example, year, month, day, hour, minute, and second, and are an example of a time section from the start of driving to the end of driving in the driving history. The road section is information indicating a section on the driving route from the start of driving to the end of driving in the driving history.

[0058] The road section is exemplified by, for example, a sequence of pairs of latitude and longitude, such as RX1, RY1, RX2, RY2, etc. For example, the sequence of pairs of latitude and longitude may be stored in another area of ​​the main memory unit 12, and the value of the road section may be a pointer (link information) that identifies that area.

[0059] The obstacle events are a list of the occurrence of obstacles in the driving history, using keywords or numerical values. For example, the keyword OK (which may be, for example, the numerical value 0) indicates that no obstacles occurred in the driving history. The keywords NG1 (for example, the numerical value 1) and NG2 (for example, the numerical value 2) indicate examples of obstacles, that is, the occurrence of specific problematic events. Examples of obstacles include driving on a detour route other than the original driving route, failure to parallel park, vehicle slippage (without an accident), accidents or damage due to slippage, and exceeding the standard number of turns when parking or stopping.

[0060] The keywords or numerical values ​​indicating the problem may be listed as they are in the elements of the problem event. Alternatively, the keywords or numerical values ​​indicating the problem may be stored in another area of ​​the main memory unit 12, and the value of the problem event may be a pointer (link information) specifying that area.

[0061] Each record in the traveling characteristics database in FIG. 4 and the traveling history database in FIG. 5 is data for each trip from the departure point to the destination, calculated from the data of one trip. However, for example, each record in the traveling characteristics database in Fig. 4 may be aggregated by statistically processing data for multiple trips (corresponding to trips from the departure point to the destination). For example, for the same individual, mental stability levels that fall within a predetermined similar range may be aggregated into one.

[0062] For example, if mental stability is rated on a 10-point scale, it may be aggregated into three levels: high, medium, and low. Here, mental stability levels of 8 to 10 are aggregated into high, 4 to 7 into medium, and 1 to 3 into low. In this case, the values ​​of the items other than mental stability can be maintained as they were before aggregation. Therefore, for example, among multiple records where the values ​​of all items other than mental stability match, those with a mental stability of "high" are combined into one record. The same applies if the mental stability is "medium" or "low."

[0063] Furthermore, for example, driving skills of different users that are the same or within a predetermined similar range may be aggregated into one. In this case, the values ​​of items other than driving skill may be maintained as they were before aggregation. Therefore, for example, multiple records that have the same values ​​for all items other than driving skill and have the same or similar driving skills will be aggregated into one record. However, the objects of aggregation are not limited to mental stability and driving skill.

[0064] In this way, when multiple records in the driving characteristics database are aggregated into one record, each record in the multiple driving history databases (FIG. 5) corresponding to the multiple records in the driving characteristics database before aggregation may be associated with the aggregated record. Alternatively, "impediment events" (see FIG. 5) among the multiple records in the multiple driving history databases corresponding to the multiple records in the driving characteristics database before aggregation may be statistically processed and aggregated.

[0065] "Problem events" can be aggregated, for example, by taking the union of all records other than OK, i.e., NG, through an OR operation (sum operation). Also, if a "problem event" is written as a numerical value such as 0, 1, or 2, the union of these numerical values ​​can be used as the aggregated "problem event." Alternatively, the maximum or average value of the "problem event" can be used as the value of the "problem event." When aggregating "problem events," events with an occurrence frequency below a reference value, i.e., "problem events" with a low occurrence frequency, can be deleted.

[0066] FIG. 6 is a diagram illustrating the configuration of a road characteristics database. The road characteristics database manages the characteristics of each road section. Each record (road characteristics data) in the road characteristics database has elements such as a section ID, a road section, and road characteristics. In addition, road characteristics have elements such as width, number of lanes, speed limit, and other elements. Examples of other elements include curve characteristics, elevation difference, visibility, etc. The structure of the driving history database is not limited to a tabular format. For example, the driving history database may be composed of keywords and values.

[0067] The section ID is identification information for identifying a road section, and can be referenced as a pointer (link information) to each record (road characteristic data) in the road characteristic database. A road section is a sequence of latitude and longitude pairs, and is information that traces the center line of a road, for example. However, a road section may also be specified by polygon information that defines the road as a polygon. However, in this embodiment, a road section is defined as a section whose width is a constant value within the allowable error range.

[0068] The width is the width of the road. The number of lanes is the number of lanes on the road. When viewed from one end point of the road section (RX1, RY1 when the section ID is 1 in Figure 6), the number of lanes on the left side LN1 and the number of lanes on the right side LN2 are Therefore, the number of lanes is set as LN1, LN2, etc. However, in road sections without a center line, the number of lanes is simply set to 1. The speed limit is the limit set for that road section. It's speed.

[0069] The other elements are defined by, for example, the degree of curve (minimum radius of curvature), elevation difference, visibility (good, poor), etc. Note that in FIG. 6, the other information is exemplified by a single element with multiple items. However, the curve characteristics, elevation difference, visibility, etc. may be set as different elements in the record.

[0070] (Processing Procedure) 7 is a flowchart illustrating route calculation A. The assistance server 2 executes route calculation A and instructs the in-vehicle device 10 of the vehicle 1 on a driving route. This process is triggered by, for example, receiving a driving route request signal from the driving assistance recipient user. In this process, the assistance server 2 first acquires the departure point, destination, and departure time from the in-vehicle device 10 (S1).

[0071] Next, the assistance server 2 acquires user characteristic data of the target user who is the driver of the vehicle 1, vehicle characteristic data of the vehicle 1, and environmental data of the geographical area including the departure point and destination (S2). Here, the target user refers to the driver who drives the vehicle 1 that receives driving assistance from the assistance server 2.

[0072] The assistance server 2 may acquire user characteristic data of the target user and vehicle characteristic data of the vehicle 1 from the in-vehicle device 10. The assistance server 2 may also store the user characteristic data of the target user in a database in association with information identifying the target user (such as user identification information). The assistance server 2 may then receive information identifying the target user from the in-vehicle device 10 and read the user characteristic data of the target user from the database. Of the user characteristic data, the assistance server 2 may calculate the user's mental characteristics, such as mental stability.

[0073] Similarly, the support server 2 may store vehicle characteristic data of the vehicle 1 in a database in association with information for identifying the vehicle 1 (such as vehicle identification information). The support server 2 may then receive information for identifying the vehicle 1 from the in-vehicle device 10 and read the vehicle characteristic data of the vehicle 1 from the database (see FIG. 13). The support server 2 may also acquire environmental data for an area including the departure point and destination from, for example, the weather server 3. The support server 2 may also estimate the time period, for example, whether the time period includes morning, noon, evening, or night, based on the departure time and the distance from the departure point to the destination, and include the estimated time period in the environmental data.

[0074] Next, the assistance server 2 calculates the off-road characteristics from the user characteristic data of the target user, the vehicle characteristic data of the vehicle 1, and the environmental data (S3). The procedure for calculating the off-road characteristics has been described with reference to Fig. 4. The off-road characteristics obtained in S3 are an example of off-road characteristics calculated based on the user characteristic data related to the driving of the target user when driving the vehicle 1, the vehicle characteristic data, and the environmental data in which the vehicle travels.

[0075] Next, the assistance server 2 searches the traveling characteristic database for past data with matching or similar traveling characteristics (S4). Here, matching traveling characteristics means that the traveling characteristic values ​​calculated in S3 match the past traveling characteristic values ​​exemplified in FIG. 4. Similarly, similar traveling characteristics means that the traveling characteristic values ​​calculated in S3 are within the range of the difference value of the allowable limit between the traveling characteristic values ​​and the past traveling characteristic values ​​exemplified in FIG. 4. The assistance server 2 may set the difference value of the allowable limit as a system parameter, for example.

[0076] The support server 2 sorts the records in the traveling characteristic database shown in FIG. 4 by the traveling characteristic value, and finds the traveling characteristic values ​​that match or are similar to the traveling characteristic values ​​calculated in S3. Past data can be searched for in a driving characteristic database. The driving characteristics acquired in S4 are past driving characteristics of vehicle 1 or other vehicles when they have traveled in the past, and can be considered to be an example of characteristics that match or are similar to the driving characteristics calculated in S3, within an acceptable limit. The difference value of the allowable limit can also be considered to be an example of an acceptable limit.

[0077] Next, the support server 2 acquires driving history data corresponding to the past driving characteristics from the driving history database (S5).The support server 2 then extracts road sections where problems occurred in the past driving history data (S6).Furthermore, the support server 2 acquires the road characteristics of the road sections where problems occurred from the road characteristics database (S7).The support server 2 then extracts excluded road sections that match the road characteristics within the allowable limits from the road characteristics database (S8).

[0078] For example, the support server 2 may extract road sections from each record in the road characteristic database whose road characteristics all match those of the road section where a problem has occurred (road characteristics in FIG. 6). The support server 2 may also determine an allowable error for each element of each record in the road characteristic database. The support server 2 may then extract road sections whose road characteristics all match those of the road section where a problem has occurred within an allowable limit. The support server 2 may also extract road sections whose road characteristics match those of the road section where a problem has occurred for a predetermined number of elements or more from the elements of the records in the road characteristic database. The support server 2 may set such an allowable error or the predetermined number of elements as a system parameter.

[0079] Then, the assistance server 2 sets a driving route in the map information database, excluding the excluded road section (S9). Finally, the assistance server 2 transmits the set driving route to the in-vehicle device 10 (S10). S9 and S10 are examples of implementing driving assistance.

[0080] If it is determined in the process of S6 that there are no road sections in the past driving history data where a problem has occurred, then in the process of S9, the support server 2 may set a driving route without excluding any excluded road sections.

[0081] (Modification of Figure 7) Here, in the process of FIG. 7 above, the assistance server 2 sets a driving route excluding the excluded road section (S9 in FIG. 7). However, the process of setting a driving route excluding the excluded road section may be executed by the in-vehicle device 10. That is, the assistance server 2 may not execute the process of S9 in FIG. 7, but may instead transmit information about the excluded road section to the in-vehicle device, and the in-vehicle device 10 may set a driving route using the map information database, excluding the excluded road section. That is, the assistance server 2 notifies the in-vehicle device 10 of the excluded road section that is not recommended, and causes the in-vehicle device 10 to provide driving assistance on the driving route along which the vehicle 1 is traveling. In this way, the assistance server 2 and the in-vehicle device 10 work together to distribute the load and provide driving assistance to the driver.

[0082] Fig. 8 is a flowchart illustrating route calculation B. In route calculation A in Fig. 7, the support server 2 sets a driving route excluding road sections where problems have occurred. On the other hand, in route calculation B, the support server 2 sets a driving route by prioritizing road sections where driving conditions were good.

[0083] In Fig. 8, the processes from S1 to S5 are the same as those in Fig. 7, and therefore their explanation will be omitted. Next, the assistance server 2 extracts good road sections where driving was good in the past driving history data (S6A). Good road sections may be, for example, road sections where obstacle events are recorded as "OK" in the driving history database of Fig. 5.

[0084] However, the support server 2 sets good road sections based on the report from the vehicle-mounted device 10. For example, the assistance server 2 may receive a report from the in-vehicle device 10 about a road section where the fuel efficiency of the vehicle 1 on that road section is higher than the average fuel efficiency of the vehicle 1, and set the road section as a good road section. Also, for example, the assistance server 2 may receive a report from the in-vehicle device 10 about a road section where the driver's mental stability is equal to or higher than a reference value, and set the road section as a good road section. The assistance server 2 may set such a reference value as a system parameter. The reference value may be, for example, the average value of the mental stability measured for the driver. Also, the reference value may be, for example, the average value of the mental stability measured for all users.

[0085] Furthermore, the support server 2 acquires the road characteristics of the good road section from the road characteristics database (S7A).Then, the support server 2 extracts from the road characteristics database priority road sections that match the road characteristics of the acquired road section within the allowable limit (S8A).The process of S8A is the same as S8.

[0086] Then, the assistance server 2 sets a driving route by giving priority to the priority road section in the map information database (S9A). Finally, the assistance server 2 transmits the set driving route to the in-vehicle device 10 (S10). S9A and S10 are examples of implementing driving assistance.

[0087] If there is no good road section in the past travel history data in the process of S6A, the support server 2 may set a travel route in the process of S9A without giving priority to good road sections.

[0088] (Modification of Figure 8) Here, in the process of FIG. 8, the assistance server 2 sets a driving route by prioritizing the priority road section (S9A in FIG. 8). However, the process of setting a driving route by prioritizing the priority road section may also be executed by the in-vehicle device 10. That is, the assistance server 2 may not execute the process of S9A in FIG. 8, but may instead transmit information about the priority road section to the in-vehicle device, and the in-vehicle device 10 may set a driving route by prioritizing the priority road section using the map information database. That is, the assistance server 2 notifies the in-vehicle device 10 of the priority road section and causes the in-vehicle device 10 to provide driving assistance on the driving route along which the vehicle 1 is traveling. In this way, the assistance server 2 and the in-vehicle device 10 work together to distribute the load and provide driving assistance to the driver.

[0089] FIG. 9 is a flowchart illustrating the caution section setting process. Since the processes from S1 to S7 in this process are the same as those in FIG. 7, their description will be omitted. After the process of S7, the assistance server 2 identifies a road section in the road characteristics database that matches the road characteristics of the road section where the obstacle occurred within an allowable limit as a caution section (S8B). The assistance server 2 then transmits information about the caution section to the in-vehicle device 10 (S10B). The information about the caution section includes, for example, information about road sections RX1, RY1, RX2, RY2, ... and information indicating the obstacle. The process of S10B is executed immediately after acquiring the departure point, destination, and departure time from the in-vehicle device 10 by the process of S1. In this case, the assistance server 2 can instruct the in-vehicle device 10 in advance to alert the driver (user) before the vehicle 1 enters the caution section. The process of S10B is an example of implementing driving assistance.

[0090] The support server 2 may transmit information about the caution section to a terminal device carried by the user. The support server 2 may also determine the ease of driving on the road section based on past obstacles that occurred in the caution section, and transmit the determined ease of driving to the in-vehicle device 10 or the terminal device carried by the user.

[0091] FIG. 10 is a flowchart illustrating a driving advice process by the in-vehicle device 10. The process of FIG. 10 may be executed by a terminal device carried by the user and linked to the in-vehicle device 10. The in-vehicle device 10 is configured to provide driving advice when the in-vehicle device 10 is powered on, the vehicle 1 starts traveling, or the driver requests driving advice. The in-vehicle device 10 executes the driving advice process in response to an instruction to start driving advice, an instruction to start navigation, etc. In this process, the in-vehicle device 10 transmits the current location and the destination to the assistance server 2 (S21). Then, the in-vehicle device 10 receives information on the caution section corresponding to the road section where the problem occurred from the assistance server 2, and stores the information in the main memory unit 12 or the like (S22).

[0092] The in-vehicle device 10 then searches for a caution section close to the current driving position (S23). The in-vehicle device 10 then determines whether the distance to the caution section close to the current driving position is within a reference value (S24). The assistance server 2 may set the reference value as a system parameter, for example. If the distance to the caution section is not within the reference value in S24, the in-vehicle device 10 returns to S23 and continues the process.

[0093] On the other hand, if the distance to the caution section falls within the reference value in S24, the in-vehicle device 10 executes driving advice processing (S25). The driving advice processing is based on a past driving history in which driving was recorded with past driving characteristics that match or are similar to the driving characteristics based on the user characteristic data of the driver, the vehicle characteristic data of the traveling vehicle 1, and the environmental data of the current driving environment. That is, the in-vehicle device 10 advises the driver of information related to obstacles that occurred in the past driving history with matching or similar driving characteristics. For example, there is a sharp curve ahead, visibility is poor, there is an elevation change, watch out for ice, the road is narrow, the number of lanes is changing, etc. In addition, in the driving advice processing, information regarding the ease of driving of the road section determined by the assistance server 2 may be presented to the driver. The processing of S25 is an example of implementing driving assistance.

[0094] Then, the in-vehicle device 10 determines whether to end the process (S26). If the in-vehicle device 10 does not receive an instruction to end the process within a predetermined waiting time, the in-vehicle device 10 returns to S23 and continues the process. On the other hand, if the in-vehicle device 10 receives an instruction to end the process within the waiting time, the in-vehicle device 10 ends the process. The assistance server 2 may set the waiting time as a system parameter, for example.

[0095] 11 is a flowchart illustrating an example of information collection processing of the in-vehicle device 10. This processing starts when the vehicle starts traveling on a road section, ends when the road section ends (when a new road section begins), and the next processing is restarted. In this processing, the in-vehicle device 10 first acquires vehicle traveling data (S31). The vehicle traveling data includes the vehicle speed, acceleration, steering angle data, accelerator opening, brake pedal angle, average fuel consumption for the road section, images of the road and objects on the road captured by the exterior camera 18B, sounds captured by the microphone, and the like.

[0096] Next, the in-vehicle device 10 acquires vital data from the vital sensor 17 and acquires facial image data of the driver from the interior camera 18A. Then, the in-vehicle device 10 calculates the mental stability level from the vital data and the facial image data (S32).

[0097] The in-vehicle device 10 has, for example, the driver's average pulse rate, average respiratory rate, average blood pressure (systolic and diastolic), etc. as reference values. The in-vehicle device 10 may store in a table the relationship between the difference (deviation) of vital data from the reference value and the mental stability. The in-vehicle device 10 may then calculate the difference of the acquired vital data from the reference value and calculate the driver's mental stability from the table. However, the in-vehicle device 10 may also store the relationship between the difference of vital data from the reference value and the mental stability as a function based on an empirical formula. The mental stability is an example of a user's mental characteristic.

[0098] 11, the in-vehicle device 10 calculates the mental stability level from the vital data and the facial image data. Instead of this process, the in-vehicle device 10 may transmit the vital data, the facial image data, etc. to the support server 2, and the support server 2 may calculate the mental stability level (see FIG. 13).

[0099] Furthermore, the in-vehicle device 10 may store, as templates for the driver's facial image data, images of the driver in a normal state, an image of the driver in a high mood, an image of the driver in a low mood, an image of the driver when the driver is surprised, and the like. The in-vehicle device 10 may then compare images obtained from the driver while the driver is driving the vehicle 1 with the templates. As a result of this comparison, the in-vehicle device 10 may estimate the driver's mental state numerically and add this to the mental stability data obtained from the vital data. For example, the in-vehicle device 10 may add 0 points to the mental stability score obtained from the vital data when the image shows a surprised state, 1 point to the low mood, 3 points to the normal state, and 5 points to the high mood. Note that the number of images is not limited to the templates, and may include images of the driver feeling fear, when the driver is unconfident in driving, or when the driver is excited. For example, when the driver is excited, driving may become unstable, and an excited state may affect driving characteristics.

[0100] For example, the in-vehicle device 10 may increase the mental stability level based on the vital data by 0 percent for an image that surprises the driver, 10 percent for an image that shows the driver in a depressed mood, 30 percent for an image that shows the driver in a normal mood, and 50 percent for an image that shows the driver in an excited mood. Note that these points and percentage increases are merely examples.

[0101] Next, the vehicle-mounted device 10 acquires current weather data from the weather server 3 (S33). Note that the vehicle-mounted device 10 may acquire weather data multiple times and accumulate and store the data, or may acquire the data only once for each road section.

[0102] Next, the in-vehicle device 10 determines whether a harmful event has occurred. As described above, harmful events for the vehicle 1 include, for example, driving on a detour route other than the original driving route, failure to parallel park, vehicle slippage (without an accident), accident or damage due to slippage, and fuel economy that is worse than the average fuel economy. The in-vehicle device 10 may detect the occurrence of driving on a detour route other than the original driving route from the difference between the driving route presented by the navigation function and the actual driving route. Furthermore, the in-vehicle device 10 may estimate the occurrence of a slip, an accident or damage due to a slip, or poor fuel economy driving from the vehicle speed, acceleration, steering angle data, accelerator opening, brake pedal angle, fuel economy of the road section, images from the exterior camera 18B, sounds, etc., and may request the driver to confirm the occurrence.

[0103] The in-vehicle device 10 then determines the type of obstacle that has occurred and records the type in the main memory unit 12 or the like (S35). Next, the in-vehicle device 10 reaches the end of the road section during travel and determines whether the road section ends (S36). If the road section does not end, the in-vehicle device 10 returns to S31 and continues processing. On the other hand, if the road section ends, the in-vehicle device 10 transmits the acquired data along with information about the travel section to the assistance server 2 (S37) and ends processing for this road section. Here, the in-vehicle device 10 includes, for example, information identifying the user who is the driver, vehicle travel data, vital signs data of the user, facial image data, mental stability, weather data, and date and time information at both ends of the road section. When the next road section is reached, the in-vehicle device 10 restarts processing from S31.

[0104] The support server 2 calculates the driving characteristics based on the vehicle driving data, the user's vital data, facial image data, mental stability, weather data, and date and time information at both ends of the road section transmitted in S37, and stores the calculated driving characteristics in a driving characteristics database (see FIG. 4). However, as described above, the in-vehicle device 10 may not calculate the driver's mental stability, and the support server 2 may calculate the mental stability based on the user's vital data and facial image data (see FIG. 13).

[0105] FIG. 12 is a flowchart illustrating the skill determination process by the support server 2. The support server 2 determines the driving skill of the user as a driver through the skill determination process. This process may be executed offline at an appropriate timing. For example, the skill determination process may be executed periodically. The driving skill determination process may be executed daily, for example. The driving skill determination process may be executed, for example, every time a road section is completed. The driving skill determination process may be executed, for example, monthly or yearly. The driving skill determination process may be executed, for example, when a problem occurs in the driver's driving.

[0106] In this process, the assistance server 2 acquires vehicle driving data for each driver (S41). For example, the assistance server 2 may acquire vehicle driving data for each road section where driving has been completed from the in-vehicle device 10. Furthermore, if the assistance server 2 periodically executes the driving skill determination process, it may acquire vehicle driving data for the latest one or more road sections from the in-vehicle device 10. Furthermore, the assistance server 2 may sequentially acquire vehicle driving data for each road section from the in-vehicle device 10 and store it in a database. Then, the in-vehicle device 10 may read out the vehicle driving data stored in the database. Note that the database may be a database managed by the assistance server 2, or may be a database managed by another computer on the network N1. The process of S41 is an example of acquiring vehicle driving data of a user from the vehicle 1 when the user drives the vehicle 1.

[0107] 11, the acquired vehicle travel data includes the vehicle speed, acceleration, steering angle data, accelerator opening, brake pedal angle, fuel efficiency on the road section, images of the road and objects on the road taken by the exterior camera 18B, sounds acquired from the microphone, etc. However, the assistance server 2 may also acquire from the in-vehicle device 10 the presence, type, frequency, etc. of obstacles on the traveled road section.

[0108] Next, the assistance server 2 calculates the driving skill based on the acquired vehicle driving data (S42). Points are calculated for the driving skill based on, for example, the smoothness of acceleration when the vehicle starts, the smoothness of deceleration by braking when the vehicle stops, the number of turns when parking, the frequency of occurrence of obstacles, etc. The processing of S42 is an example of determining the driving skill based on the vehicle driving data of the user.

[0109] Next, the assistance server 2 stores the calculated driving skill in a database in association with information for identifying the driver (such as user identification information) (S43). The stored driving skill is used when calculating the driving characteristics (for example, the processes of S2 and S3 in FIG. 7). The calculated driving skill is also stored as a sub-item of the user characteristic data in the driving characteristics database (FIG. 4) in association with the driving history data.

[0110] FIG. 13 is a flowchart illustrating a mental stability calculation process performed by the support server 2. The support server 2 calculates the mental stability as an example of the mental characteristics of the user as a driver through the mental stability calculation process. This process may be performed offline at an appropriate timing. For example, the mental stability calculation process may be performed periodically, such as daily, each time the user gets into the vehicle 1. The mental stability calculation process may also be performed, for example, each time the user completes driving a road section. The mental stability calculation process may also be performed, for example, monthly or annually. The mental stability calculation process may also be performed, for example, when a problem occurs in the driver's driving.

[0111] In this process, the support server 2 acquires vital data and facial image data for each driver (S51). The support server 2 may acquire the vital data and facial image data for each driver from a terminal device carried by the user or a terminal device linked to the in-vehicle device 10. The process of S51 is an example of acquiring vital data of the user when the user drives the vehicle 1 from the vehicle or a terminal device carried by the user.

[0112] Next, the support server 2 performs the following based on the acquired vital data and facial image data for each driver: The mental stability is calculated (S52). The processes of S51 and S52 are the same as the process of S32 executed by the in-vehicle device 10.

[0113] Next, the assistance server 2 stores the calculated mental stability in a database in association with information that identifies the driver (such as user identification information) (53). The stored mental stability is used when calculating the driving characteristics (for example, the processes of S2 and S3 in FIG. 7). The calculated mental stability is also stored as a sub-item of the user characteristic data in the driving characteristics database (FIG. 4) in association with the driving history data.

[0114] (Effects of the embodiment) As described above, the assistance server 2 acquires user characteristic data related to the driving of the user who is the driver of the vehicle 1-1, vehicle characteristic data of the vehicle 1-1, and environmental data (e.g., weather data) in which the vehicle 1-1 travels. The assistance server 2 then acquires the driving characteristics that are determined based on these data. When the acquired driving characteristics match or are similar to past driving characteristics of the vehicle 1-1 or another vehicle 1-2 within an acceptable limit, the assistance server 2 acquires the driving history of when the vehicle 1-1 or another vehicle 1-2 traveled in the past using those past driving characteristics. The assistance server 2 then provides driving assistance on the driving route traveled by the vehicle 1-1 based on the acquired driving history.

[0115] As described above, in this embodiment, the assistance server 2 provides driving assistance by referring to past driving history in which the driving characteristics reflecting the user characteristic data, vehicle characteristic data, and environmental data match or are similar. Therefore, the assistance server 2 can provide driving assistance that matches the user characteristic data, vehicle characteristic data, and environmental data at that time. For example, the assistance server 2 can provide driving assistance that matches the driver's skill, mental state, ease of driving the vehicle such as the vehicle size, weather, road width, and other conditions.

[0116] Furthermore, the assistance server 2 determines the ease of driving on the route along which the vehicle 1 is traveling based on the acquired driving history, and provides driving assistance based on the determined ease of driving. That is, the assistance server 2 can provide the driver of the vehicle 1 with information on the ease of driving on the route along which the vehicle 1 is traveling.

[0117] Furthermore, the support server 2 acquires vehicle driving data of the user when the user, who is the driver, drives the vehicle 1 from the vehicle 1, and determines or calculates the driving skill based on the acquired vehicle driving data. Therefore, the support server 2 can determine or calculate the driving skill of the driver at an appropriate time and based on evidence.

[0118] Furthermore, the support server 2 acquires vital data of the user when the user, who is the driver, drives the vehicle 1 from the vehicle 1 or a terminal device carried by the user. Then, the support server 2 determines the mental stability, which is the mental characteristic of the user, based on the acquired vital data of the user. Therefore, the support server 2 can determine or calculate the mental characteristic of the driver at an appropriate time and based on evidence.

[0119] The assistance server 2 also acquires information specifying the driving route along which the vehicle 1 will travel, and date and time information specifying the scheduled date and time of departure or the scheduled date and time of arrival at the destination, from the vehicle or a terminal device carried by the user (S1 in FIG. 7). The assistance server 2 also acquires weather data along the driving route along which the vehicle 1 will travel during the time period specified by the date and time information (S2 in FIG. 7). The assistance server 2 then calculates the driving characteristics based on the acquired weather data, i.e., environmental data, and can provide driving assistance that reflects the driving environment.

[0120] The support server 2 also extracts road sections that are difficult to travel from the road sections identified in the map database as excluded road sections based on the above-mentioned travel characteristics, and determines the travel route to be traveled. Therefore, the assistance server 2 can provide driving assistance by using the above-mentioned driving characteristics to avoid excluded road sections that have caused problems in the past.

[0121] In addition, the support server 2 recommends priority road sections (road sections with fewer obstacles, road sections where driving was good) to the driver based on the driving history of the driver, vehicle, and environment that are the target of driving support, among the road sections identified in the map database. Therefore, the support server 2 can provide the vehicle 1 with a suitable route that is suitable and has the least amount of obstacles. It can provide driving assistance on favorable driving routes.

[0122] Furthermore, the support server 2 extracts road sections identified in the map database where there are obstacles to driving as caution sections based on the above-mentioned driving characteristics. Therefore, the support server 2 can alert the user before the vehicle 1 enters the caution section, thereby preventing the occurrence of obstacles.

[0123] <Embodiment 2> A driving assistance system 50 according to the second embodiment will be described below with reference to Fig. 14. The driving assistance system 50 of the second embodiment has a function of utilizing information from a traffic information server added to the driving assistance system 50 of the first embodiment.

[0124] The traffic information server provides traffic information data. The traffic information data is, for example, data called cross-sectional traffic volume. The cross-sectional traffic volume is, for example, data including location information, time, and traffic volume measured at cross-sectional traffic volume measurement points nationwide at predetermined intervals (for example, every five minutes). The traffic information data may also be data on road sections where congestion is occurring. The support server 2 can obtain traffic information including the traffic volume or the presence and extent of congestion on the road section on which the vehicle 1 is traveling, for example, by accessing the traffic information server via an API.

[0125] In this embodiment, the weather data obtained from the weather server 3 and the traffic information obtained from the traffic information server in Fig. 1 are called environmental data. However, the environmental data is not limited to weather data and traffic information data. The environmental data may include, for example, information that distinguishes the time period (e.g., morning, daytime, night, evening, etc.) in which the vehicle 1 is traveling.

[0126] 14 is a flowchart illustrating the processing of the assistance server 2 of this embodiment. In this processing, the assistance server 2 first acquires the departure point, destination, and departure time from the in-vehicle device 10, and then determines a travel route for the vehicle 1 (S1C). The travel route determined in S1C is a tentative travel route that does not take into account the off-road characteristics described in the first embodiment.

[0127] Next, the assistance server 2 acquires user characteristics of the target user who is the driver of the vehicle 1, vehicle characteristics of the vehicle 1, and environmental data for a geographical area including the departure point and destination (S2C). Of the processes in S2C, the process of acquiring the user characteristic data of the target user who is the driver and the vehicle characteristic data of the vehicle 1 is the same as in embodiment 1 (S2 in FIG. 7). Meanwhile, the environmental data in this embodiment includes traffic information on the driving route determined in S1C in addition to the weather data in embodiment 1. That is, the assistance server 2 acquires traffic information on the driving route determined in S1C from the traffic information server and stores it as environmental data together with the weather data acquired from the weather server 3.

[0128] Next, the assistance server 2 calculates the driving characteristics from the user characteristic data of the target user, the vehicle characteristic data of the vehicle 1, and the environmental data (S3C). The procedure for calculating the driving characteristics is explained in FIG. 4. However, the calculation of the driving characteristics in S3C reflects the traffic information on the driving route determined in S1C. For example, when there is no traffic jam, the contribution of the traffic information is evaluated as 10 points, and when there is traffic jam, the contribution of the traffic information is evaluated as 0 points.

[0129] The support server 2 then executes the processes from S4 to S8. The processes from S4 to S8 are the same as the processes from S4 to S8 in FIG. 7. The support server 2 then excludes the excluded road section and sets the driving route again (S9C). That is, the support server 2 first sets a tentative driving route in the process of S1C, obtains traffic information for the tentative driving route, and then calculates the driving characteristics. The support server 2 then sets the excluded road section based on the driving characteristics, and sets the driving route again.

[0130] Then, the assistance server 2 transmits the driving route to the in-vehicle device (S10). Through this procedure, the assistance server 2 calculates the driving characteristics based on the tentative driving route, reflects the traffic information, sets the driving route, and can provide driving assistance. (Modification of the second embodiment) In the second embodiment, the support server 2 first sets a tentative driving route in the process of S1C, obtains traffic information on the tentative driving route, and then calculates the driving characteristics (S3C above).The support server 2 then sets excluded road sections based on the driving characteristics, and sets the driving route again.However, instead of this process, the support server 2 may incorporate past traffic congestion records into the environmental data. For example, the support server 2 uses past traffic congestion information for the area including the departure point and destination, where the target user departs under the same or similar conditions, such as the date, time, time zone, and day of the week, as a predicted value, to obtain estimated traffic congestion information. In this case, the traffic congestion information does not need to be detailed for each road section. For example, the traffic congestion information may indicate the degree of congestion along the entire route from the departure point to the destination in multiple stages, such as (1) heavy congestion, (2) some congestion, (3) light congestion, and (4) no congestion. The traffic congestion information may also be in two stages: "congestion" and "no congestion." The support server 2 then reflects the estimated traffic congestion information in the driving characteristics in the process of S3C described above. Then, in the process of S4 above, the assistance server 2 searches the driving characteristics database for past data with matching or similar driving characteristics. In this case, each record in the driving characteristics database includes actual traffic congestion information in the environmental data. This actual traffic congestion information also indicates the degree of congestion in multiple stages. In this way, the assistance server 2 can retrieve past data with matching or similar driving characteristics from the driving characteristics database, reflecting the environmental data incorporating the traffic congestion information, without calculating a tentative driving route as in the process of S1C above.

[0131] <Other embodiments> In the above-described first and second embodiments, the assistance server 2 calculates the driving characteristics based on the user characteristic data, vehicle characteristic data, and environmental data of the target user who is the target of driving assistance. Then, the assistance server 2 obtains a driving history corresponding to past driving characteristics that are the same as or similar to the calculated driving characteristics, and provides driving assistance to the target user based on the driving history.

[0132] Instead of this method, the assistance server 2 may obtain previously accumulated user characteristic data, vehicle characteristic data, and environmental data that are identical or similar to the user characteristic data, vehicle characteristic data, and environmental data of the target user who is the target of driving assistance.The assistance server 2 may then obtain a driving history of driving using user characteristic data, vehicle characteristic data, and environmental data that are identical or similar to the user characteristic data, vehicle characteristic data, and environmental data of the target user.

[0133] That is, in the above-mentioned first and second embodiments, the user characteristic data, vehicle characteristic data, and environmental data are aggregated as the driving characteristics, and the driving characteristics are used as a comprehensive evaluation index. In contrast to this, the assistance server 2 may individually compare the user characteristic data of the target user, the vehicle characteristic data of the vehicle 1 driven by the target user, and the environmental data with past data. Then, the assistance server 2 may obtain the driving history to be referenced in the driving assistance based on the results of these comparisons. .

[0134] In this case, the support server 2 may compare each of the sub-items illustrated in FIG. 4 with the target user's data and past data, and determine user characteristic data, vehicle characteristic data, and environmental data for which each sub-item is identical or similar. Here, the user characteristic data, vehicle characteristic data, and environmental data can be referred to as a major item that combines sub-items. If all of the sub-items of a major item (user characteristic data, vehicle characteristic data, or environmental data, respectively) are identical or similar between the target user and past data, the support server 2 may determine that the major item is identical or similar. Furthermore, even if some of the sub-items in a major item are not identical or similar between the target user's data and past data, the support server 2 may determine that the major item is similar if a specified number or more of the sub-items are identical or similar.

[0135] The support server 2 may also comprehensively evaluate sub-items in each of the user characteristic data, vehicle characteristic data, and environmental data, and determine evaluation points for each of the user characteristic data, vehicle characteristic data, and environmental data.The support server 2 may then obtain user characteristic data, vehicle characteristic data, and environmental data of past data whose evaluation points are the same as or similar to those of the target user.

[0136] In the above comparison of the user characteristic data, vehicle characteristic data, and environmental data of the target user with past data, the support server 2 may employ a collaborative filtering procedure.

[0137] <Computer-readable recording medium> A program that causes a computer or other machine or device (hereinafter referred to as a computer, etc.) to realize any of the above functions can be recorded on a computer-readable recording medium. Then, by having the computer, etc. read and execute the program from this recording medium, the function can be provided.

[0138] Here, a computer-readable recording medium refers to a recording medium that stores information such as data and programs electrically, magnetically, optically, mechanically, or chemically and can be read by a computer. Among such recording media, those that can be removed from a computer include, for example, flexible disks, magneto-optical disks, CDs (Compact Discs), DVDs (Digital Versatile Discs), Blu-ray Discs, and memory cards such as flash memory. Furthermore, examples of recording media that are fixed to a computer include hard disks and ROMs (Read Only Memory). Furthermore, SSDs (Solid State Drives) ) can be used as a recording medium that can be removed from a computer or the like, or as a recording medium that is fixed to a computer or the like. [Explanation of symbols]

[0139] 1 vehicle 2 Support Server 3 Weather Server 10 Onboard equipment 11 CPU 12 Main memory 13 External memory unit 14 Display section 15 Control section 16 Communications Department 17 Vital Sensor 18A In-car camera 18B Exterior camera

Claims

1. Acquire driving characteristics that are determined based on user characteristics related to driving when a user drives a vehicle, vehicle characteristics of the vehicle, and environmental data in which the vehicle travels; When the acquired off-road characteristics match or are similar to past off-road characteristics when the vehicle or another vehicle has traveled in the past within an acceptable limit, a travel history when the vehicle or another vehicle has traveled according to the past off-road characteristics is acquired; An information processing device including a control unit that performs driving assistance on a driving route on which the vehicle is traveling based on the acquired driving history.

2. The information processing device according to claim 1 , wherein the control unit determines drivability on a driving route on which the vehicle is traveling based on the acquired driving history, and performs the driving assistance based on the determined drivability.

3. the user characteristics include driving skills; the control unit acquires vehicle driving data of the user when the user drives the vehicle; The information processing device according to claim 1 , wherein the driving skill is determined based on acquired vehicle driving data of the user.

4. the user characteristics include mental characteristics of the user when the user drives the vehicle; The control unit acquires vital data of the user when the user drives the vehicle, The information processing device according to claim 1 , wherein the information processing device determines the mental characteristics of the user based on the acquired vital data of the user.

5. the environmental data includes at least one of weather data and time of day when the vehicle is traveling; The control unit acquires information specifying a route along which the vehicle will travel and date and time information specifying a scheduled departure date and time or a scheduled arrival date and time at a destination, acquiring the environmental data on the travel route along which the vehicle travels based on the date and time information; The information processing device according to claim 1 , wherein the traveling characteristics are calculated based on the acquired environmental data.

6. The driving assistance includes route guidance.

2. The information processing device according to claim 1, wherein the control unit extracts, from among the road sections identified in the map database, road sections that have caused problems in driving based on the acquired driving history as excluded road sections, and determines the driving route for the vehicle by excluding road sections with road characteristics that are the same as or similar to the extracted excluded road sections.

7. The driving assistance includes route guidance, 2. The information processing device according to claim 1, wherein the control unit extracts, from among the road sections identified in the map database, priority road sections to be recommended to the user of the vehicle traveling with the driving characteristics based on the acquired driving history, and determines a driving route for the vehicle by giving priority to road sections with road characteristics that are the same as or similar to the road characteristics of the extracted priority road sections.

8. The driving assistance includes driving advice, The control unit extracts, from among road sections identified in a map database, road sections where driving has been difficult based on the acquired driving history as caution-required sections, and The information processing device according to claim 1 , wherein the information processing device issues a warning to the user before the user enters a desired section.

9. the information processing device is an in-vehicle device that cooperates with a device mounted in a vehicle via a network, The control unit, the control unit identifies, from among the road sections identified in the map database, priority road sections to be recommended to the user of the vehicle or excluded road sections that cannot be recommended based on the acquired driving history, and notifies the in-vehicle device of the identified road sections; The information processing device according to claim 1 , wherein the in-vehicle device is configured to perform the driving assistance on a route on which the vehicle is traveling.

10. a computer acquires driving characteristics determined based on user characteristics related to driving when a user drives a vehicle, vehicle characteristics of the vehicle, and environmental data in which the vehicle travels; When the acquired off-road characteristics match or are similar to past off-road characteristics when the vehicle or another vehicle has traveled in the past within an acceptable limit, a travel history when the vehicle or another vehicle has traveled according to the past off-road characteristics is acquired; An information processing method that provides driving assistance on a route traveled by the vehicle based on the acquired driving history.

11. a computer acquires driving characteristics determined based on user characteristics related to driving when a user drives a vehicle, vehicle characteristics of the vehicle, and environmental data in which the vehicle travels; When the acquired off-road characteristics match or are similar to past off-road characteristics when the vehicle or another vehicle has traveled in the past within an acceptable limit, a travel history when the vehicle or another vehicle has traveled according to the past off-road characteristics is acquired; A program for executing the execution of providing driving assistance on a route along which the vehicle is traveling based on the acquired driving history.

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