Information processing device and information processing method

Abstract

An information processing device (100, 200, 400, 500, 600, 700) comprises: a positioning information acquisition unit (101) that acquires positioning information on a vehicle; a moving body information acquisition unit (102) that acquires moving body information indicating the relative position of the vehicle and a moving body around the vehicle; a concentration degree information acquisition unit (104) that acquires concentration degree information indicating the driving concentration degree of a driver who drives the vehicle during traveling; and an association information generation unit (107) that generates concentration degree association information in which the positioning information acquired by the positioning information acquisition unit (101), the moving body information acquired by the moving body information acquisition unit (102), and the concentration degree information acquired by the concentration degree information acquisition unit (104) are associated with one another.

Description

Information processing device and information processing method

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

[0002] A driving assistance device for a vehicle that optimally assists a driver in driving operations according to the driver's level of concentration on driving operations has been disclosed (see, for example, Patent Document 1). This driving assistance device detects the driver's line of sight using an imaging device, and when it detects that the driver's line of sight is not directed ahead of the vehicle, it determines that the driver's level of concentration on driving operations is low and assists the driving operation by generating a steering assist force using a steering actuator.

[0003] Japanese Patent Application Laid-Open No. 2000-211543

[0004] In general, the degree of a driver's concentration on driving may change due to external factors, and there is a problem in that it is difficult to predict changes in the degree of concentration on driving.

[0005] The present disclosure was made in response to the recognition of the above-mentioned problem, and aims to provide an information processing device and an information processing method that can generate information for predicting changes in a driver's level of concentration on driving.

[0006] The information processing device of the present disclosure is characterized by comprising a positioning information acquisition unit that acquires positioning information of a vehicle, a mobile body information acquisition unit that acquires mobile body information indicating the relative position of the vehicle and mobile bodies around the vehicle, a concentration information acquisition unit that acquires concentration level information that indicates the driving concentration level of a driver operating the vehicle while it is in motion, and a related information generation unit that generates concentration level related information that correlates the positioning information acquired by the positioning information acquisition unit, the mobile body information acquired by the mobile body information acquisition unit, and the concentration level information acquired by the concentration level information acquisition unit.

[0007] The information processing device of the present disclosure can generate concentration-related information in which positioning information, mobile object information, and concentration information are mutually associated, for example, as information for predicting changes in a driver's driving concentration level based on mobile object information and concentration information.

[0008] 12A, 12B, and 12C are diagrams illustrating an example of an image that the information processing system according to the third embodiment displays on a display device based on map-related information. FIG. 12B is a block diagram illustrating an example of a process performed by the information processing device according to the third embodiment. FIG. 12C is a block diagram illustrating an example of a process performed by the information processing device according to the third embodiment. FIG. 12A is a block diagram illustrating an example of a process performed by the information processing device according to the third embodiment. FIG. 12B is a block diagram illustrating an example of a process performed by the information processing device according to the third embodiment. FIG. 12C is a block diagram illustrating an example of an image that the information processing system according to the third embodiment displays on a display device based on map-related information. FIG. 12B is a block diagram illustrating an example of a process performed by the information processing device according to the fourth embodiment. FIG. 12C is a block diagram illustrating an example of a process performed by the information processing device according to the fourth embodiment. FIG. 12B is a block diagram illustrating an example of a process performed by the information processing device according to the fourth embodiment. FIG. 12C is a block diagram illustrating an example of a process performed by the information processing device according to the fifth embodiment. FIG. 10 is a diagram showing a section where driving assistance is provided by an information processing device according to embodiment 5. FIG. 11 is a block diagram showing a schematic configuration of an information processing system according to embodiment 6. FIG. 12 is a flowchart showing an example of processing performed by an information processing device according to embodiment 6 to output concentration level related information to a learned model generation device. FIG. 13 is a flowchart showing an example of processing performed by an information processing device according to embodiment 6 to generate vehicle control information using a learned model. FIG. 14 is a block diagram showing a schematic configuration of an information processing system according to embodiment 7. FIG. 15 is a flowchart showing an example of processing performed by an information processing device according to embodiment 7.

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Embodiment 1. First, a schematic configuration of an information processing system 1 according to embodiment 1 will be described with reference to FIG. 1. FIG. 1 is a block diagram showing a schematic configuration of the information processing system 1 according to embodiment 1. The information processing system 1 is a system for generating concentration-related information related to a driver's concentration on driving while driving a vehicle using an information processing device 100. As shown in FIG. 1, the information processing system 1 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, a storage device 60, and the information processing device 100, which are connected wirelessly or by wire so as to be able to communicate with each other. In embodiment 1, the term "driver" simply refers to a specific driver for whom the information processing device 100 generates concentration-related information, and the term "vehicle" simply refers to a specific vehicle driven by the driver.

[0010] The positioning device 10 measures the position of a vehicle. For example, the positioning device 10 has a GNSS (Global Navigation Satellite System) receiver, receives GNSS signals from GPS (Global Positioning System) satellites, and measures the absolute position and time of the vehicle based on the received signals. The positioning device 10 outputs positioning information, which is the positioning result of the vehicle, to the information processing device 100.

[0011] The peripheral object detection device 20 detects peripheral objects around the vehicle and generates information according to the detection results. For example, the peripheral object detection device 20 is configured with an image recognition device that performs image recognition using image information acquired by an imaging device such as a millimeter-wave radar, an infrared radar, an ultrasonic sensor, a LiDAR (Light Detection and Ranging) sensor, or a camera installed in the vehicle. For example, the peripheral object detection device 20 detects moving objects around the vehicle and calculates the relative positions of the vehicle and the moving objects to generate moving object information indicating the relative positions of the vehicle and the moving objects around the vehicle. Furthermore, for example, the peripheral object detection device 20 detects lane markings on the road on which the vehicle is traveling and generates lane marking information indicating the relative positions of the vehicle and the lane markings around the vehicle. The peripheral object detection device 20 outputs the generated information to the information processing device 100.

[0012] The imaging device 30 captures an image of the interior of a traveling vehicle to generate image information of the driver of the vehicle. For example, the imaging device 30 has an imaging element (image sensor) and a lens (not shown), and generates image information by converting light captured through the lens into a signal using the imaging element. The imaging element may be a solid-state imaging element such as a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary Metal Oxide Semiconductor) image sensor. For example, the imaging device 30 is provided inside the vehicle and captures images of the interior of the vehicle at a preset frame rate to generate image information of the driver seated in the driver's seat. The imaging device 30 outputs the generated image information to the information processing device 100. Note that in the first embodiment, a "traveling vehicle" refers to a vehicle in which a driver intending to drive the vehicle is seated in the driver's seat, including not only vehicles in a moving state but also vehicles in a stopped state.

[0013] The storage device 60 acquires and stores information from the information processing device 100. For example, the storage device 60 is configured with an SSD (Solid State Drive), an HDD (Hard Disk Drive), a NAS (Network Attached Storage), another computer, etc. The storage device 60 may be provided in the vehicle, or some or all of its functions may be provided outside the vehicle, and may be communicatively connected to the information processing device 100 provided in the vehicle via a vehicle communication device (not shown). The storage device 60 may also be configured to acquire and store information from a plurality of information processing devices (not shown) that generate concentration-related information regarding the driving concentration level of each driver while driving a plurality of vehicles.

[0014] The information processing device 100 is a device for generating concentration-related information in which the positioning information, mobile object information, and concentration information indicating the driver's concentration on driving while driving a vehicle are mutually associated, based on positioning information, mobile object information, and concentration information. The information processing device 100 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration information acquisition unit 104, and a related information generation unit 107.

[0015] The positioning information acquisition unit 101 acquires positioning information of the vehicle. For example, the positioning information acquisition unit 101 acquires positioning information indicating the absolute position of the vehicle using latitude and longitude and time information indicating the positioning time based on information from the positioning device 10. Note that the positioning information acquisition unit is not limited to acquiring positioning information based only on the GNSS signal from the positioning device 10. The positioning information acquisition unit may be configured to acquire positioning information based on other information. For example, the positioning information acquisition unit may be configured to acquire positioning information based on information from a speed sensor, acceleration sensor, gyro sensor, or other sensor (not shown) provided in the vehicle, or may be configured to acquire positioning information based on information from these sensors and GNSS signals. Alternatively, the positioning information acquisition unit may be configured to acquire positioning information using RTK (Real Time Kinematic)-GNSS, which corrects GNSS signals based on signals transmitted from a base station. Alternatively, the positioning information acquisition unit may be configured to acquire map information containing road information including information on the number of lanes on the road on which the vehicle is traveling from a database or the like in advance, and acquire positioning information using a high-precision locator capable of sub-meter-level positioning. For example, a positioning information acquisition unit using a high-precision locator may be configured to acquire positioning information for identifying the lane on which the vehicle is traveling from the high-precision locator.

[0016] The mobile object information acquisition unit 102 acquires mobile object information indicating the relative positions of the vehicle and mobile objects around the vehicle. For example, the mobile object information acquisition unit 102 acquires mobile object information indicating the relative distance between the vehicle and other vehicles as mobile objects around the vehicle. Specifically, the mobile object information acquisition unit 102 acquires mobile object information indicating the relative distance between the vehicle and other vehicles traveling in a specific lane as mobile objects around the vehicle. More specifically, the mobile object information acquisition unit 102 acquires mobile object information indicating the relative distance between the vehicle and other preceding vehicles traveling in the same lane as the vehicle as mobile objects around the vehicle. Furthermore, even more specifically, the mobile object information acquisition unit 102 acquires mobile object information indicating the relative distance between the vehicle and other following vehicles traveling in the same lane as the vehicle as mobile objects around the vehicle. More specifically, the mobile object information acquisition unit 102 acquires mobile object information indicating the relative distance between the vehicle and other vehicles traveling in an adjacent lane to the vehicle as mobile objects around the vehicle. More specifically, the mobile object information acquisition unit 102 acquires mobile object information indicating the relative distance between the vehicle and other vehicles traveling in an oncoming lane to the vehicle as mobile objects around the vehicle. Note that the mobile objects around the vehicle may be any mobile objects that can approach the traveling vehicle, and are not limited to other vehicles, but may also be pedestrians.

[0017] Hereinafter, more specific examples of the mobile object information acquired by the mobile object information acquisition unit 102 will be described. For example, the mobile object information related to another vehicle includes information indicated by one or more of the following indicators 1 to 8. In this description, the vehicle that is the reference for the mobile object information will also be referred to as the subject vehicle. Indicator 1) Presence or absence of vehicles in front and behind the subject vehicle's lane: Presence or absence of other vehicles traveling in front and behind the subject vehicle in the subject vehicle's lane, and the duration of this state (including the duration for which a state in which there are no other vehicles in front and behind the subject vehicle is sustained). Presence or absence of other vehicles in front and behind the subject vehicle refers, for example, to whether or not there is another vehicle within a distance that the subject vehicle will reach in 5 seconds, or whether or not there is another vehicle within a predetermined distance, for example, 250 meters. Indicator 2) Distance between vehicles in front and behind the subject vehicle's lane: The distance between the subject vehicle and another vehicle in front of the subject vehicle traveling in the subject vehicle's lane, or the time it takes for the subject vehicle to reach the position of the other vehicle in front, and the duration of this state. Indicator 3) Presence or absence of nearby vehicles in the subject vehicle's lane: Presence or absence of nearby vehicles traveling in front and behind the subject vehicle's lane, and the duration of this state. An adjacent vehicle is, for example, another vehicle that is within a distance that the host vehicle will reach in three seconds, or another vehicle traveling within 30 meters in front of or behind the host vehicle. Indicator 4) Presence or absence of a vehicle traveling in an adjacent lane: The presence or absence of another adjacent vehicle traveling in a lane adjacent to the host vehicle's lane, and the duration of that state. An adjacent vehicle is, for example, a vehicle that is within a distance that the host vehicle will reach in three seconds. Indicator 5) Presence or absence of a vehicle requiring caution: The presence or absence of a vehicle requiring caution, the presence or absence of an adjacent vehicle traveling parallel, the presence or absence of another vehicle changing lanes into the host vehicle's lane, and the presence or absence of a nearby vehicle exhibiting erratic behavior. A vehicle requiring caution is a vehicle traveling in very close proximity in the host vehicle's lane, for example, another vehicle within a distance that the host vehicle will reach in one second. An adjacent vehicle traveling parallel is a vehicle whose inter-vehicle distance would be within the host vehicle's vehicle length if the host vehicle were to move into the adjacent lane. Indicator 6) Relative position of other vehicles: Relative position information between the host vehicle and one or more other vehicles, and changes in relative position over time. Index 7) Surrounding object detection information: Information on surrounding objects detected by a surrounding object detection device and changes in the information over time. Index 8) Image information: Image information and video information obtained by photographing the area around the vehicle.

[0018] The image information acquisition unit 103 acquires image information of a driver who is driving a vehicle while it is moving. For example, the image information acquisition unit 103 acquires image information including at least a part of the driver's face based on information from the imaging device 30.

[0019] The concentration level information acquisition unit 104 acquires concentration level information indicating the driving concentration level of a driver who drives a vehicle. For example, the concentration level information acquisition unit 104 acquires concentration level information indicating the driving concentration level of the driver based on image information acquired by the image information acquisition unit 103. Specifically, the concentration level information acquisition unit 104 extracts the driver's eyelid opening degree, gaze direction, facial orientation, facial expression, posture, presence or absence of reflexes due to drowsiness, and other external characteristics from the image information acquired by the image information acquisition unit 103, and acquires concentration level information based on the extracted information. The concentration level information acquisition unit 104 may be configured to quantify the external characteristics and acquire concentration level information based on the numerical values ​​and changes in the numerical values ​​obtained by quantifying the external characteristics, or may be configured to acquire concentration level information using a trained model that learns in advance based on input of image information of a specific or unspecified driver and information indicating the driving concentration level, and outputs the driver's driving concentration level in response to input image information of the driver.

[0020] For example, the concentration level information acquisition unit 104 acquires, as time-series data, the driver's alertness level, which indicates whether the driver is awake or not and the level of alertness, the inattentiveness level, which indicates whether the driver is in an inattentive state while driving or the level of inattentiveness, and the absentmindedness level, which indicates whether the driver is in an absentminded state without sufficient concentration on driving or the level of absentmindedness, based on the image information acquired by the image information acquisition unit 103. For example, the alertness level is calculated based on the detection results of the blink duration, eyelid opening degree, facial expression, yawning, microsleep, and posture. Specifically, the alertness level is calculated as a value lower than that of the fully awake state when one or more of the following are detected: a blinking time longer than a predetermined threshold, a long blinking time more frequently than a predetermined threshold, an eyelid opening degree lower than a predetermined threshold, a facial expression indicating a drowsy state, the occurrence of yawning, the occurrence of microsleep, and a tilted posture, with the alertness level being 1 for a fully awake state and 0 for a sleeping state. In addition, the level of alertness may be calculated as 0 in a state where it is difficult to continue driving, such as when microsleeps occur multiple times within a few seconds.

[0021] Furthermore, for example, the degree of inattentiveness is calculated based on the detection results of the gaze direction and facial direction. Specifically, the degree of inattentiveness is calculated as 1 when the driver is not looking aside at all and 0 when the driver is completely looking aside. When one or more of the following is detected, the degree of inattentiveness is calculated as a value lower than that when the driver is not looking aside at all: the percentage of time the gaze direction is other than the forward direction is equal to or greater than a predetermined threshold; the length of time the gaze direction is continuously other than the forward direction is equal to or greater than a predetermined threshold; the percentage of time the face direction is other than the forward direction is equal to or greater than a predetermined threshold; or the length of time the face direction is continuously other than the forward direction is equal to or greater than a predetermined threshold. Note that the degree of inattentiveness may be calculated as 0 when the gaze direction is continuously other than the forward direction for three seconds or more. In addition, in the first embodiment, "the gaze direction is other than the forward direction" refers to a state in which the angular difference between the gaze direction and the traveling direction of the vehicle is equal to or greater than a predetermined threshold, and "the face direction is other than the forward direction" refers to a state in which the angular difference between the face direction and the traveling direction of the vehicle is equal to or greater than a predetermined threshold.

[0022] Furthermore, for example, the absentmindedness is calculated based on the detection result of the gaze direction. Specifically, the absentmindedness is calculated as 1 when the driver is concentrating on driving and 0 when the driver is driving absentmindedly without concentrating on driving at all. When one or more of the following is detected, the absentmindedness is calculated as a value lower than that of the state when the driver is concentrating on driving: the amount of change in the gaze direction is less than a predetermined threshold, the number of changes in the gaze direction is less than a predetermined threshold, or the variation (variance) of changes in the gaze direction is less than a predetermined threshold. Specifically, the state when the driver is driving absentmindedly without concentrating on driving is when the driver's gaze remains motionless for 10 seconds or more.

[0023] For example, the concentration level information acquisition unit 104 performs calculations based on a preset algorithm on the quantified levels of alertness, inattention, and absentmindedness, and acquires the concentration level information that is the calculation result. Specifically, the concentration level information acquisition unit 104 calculates the total value of the quantified levels of alertness, inattention, and absentmindedness, thereby acquiring the concentration level information that is the calculation result as time-series data. Also, specifically, the concentration level information acquisition unit 104 calculates the minimum value of the quantified levels of alertness, inattention, and absentmindedness, thereby acquiring the concentration level information that is the calculation result as time-series data. Also, specifically, the concentration level information acquisition unit 104 calculates the average value of the quantified levels of alertness, inattention, and absentmindedness, thereby acquiring the concentration level information that is the calculation result as time-series data.

[0024] Specifically, the concentration level information acquisition unit 104 multiplies the quantified values ​​of the alertness, the inattentiveness, and the absentmindedness to acquire the calculated concentration level information as time-series data. The concentration level information acquisition unit 104 may be configured to perform calculations based on the quantified values ​​of the alertness, the inattentiveness, and the absentmindedness after assigning different weights to each of them, or may be configured to acquire the concentration level information based on the alertness, the inattentiveness, and the absentmindedness through calculations based on other algorithms. For example, the driving concentration level is calculated as a value ranging from 0 to 1, with 1 representing the highest level of driving concentration and 0 representing the lowest level of driving concentration. The concentration level information acquisition unit may be configured to calculate the alertness, the inattentiveness, the absentmindedness, and the driving concentration level as two or more discrete values, or as continuous values.

[0025] Furthermore, when the concentration information acquisition unit is configured to calculate the alertness, the degree of inattention, and the degree of absentmindedness as three or more discrete or continuous values, each of the above-mentioned thresholds may be set to a plurality of values. Furthermore, for example, when the alertness is calculated as a continuous value ranging from 0 to 1 and the alertness is 0.5 or greater, the concentration information acquisition unit may calculate the driving concentration level as a value corresponding to the alertness value, and when the alertness is less than 0.5, the driving concentration level may be calculated as 0 regardless of the alertness value, assuming that driving is difficult. Furthermore, for example, when the alertness is calculated as a continuous value ranging from 0 to 1 and the alertness is 0.75 or greater, the concentration information acquisition unit may calculate the driving concentration level as 1 regardless of the alertness value. Furthermore, the concentration information acquisition unit 104 may be configured to acquire information on one or a combination of one or more of the alertness, the degree of inattention, and the degree of absentmindedness as the concentration information.

[0026] The related information generation unit 107 generates concentration-related information in which the positioning information acquired by the positioning information acquisition unit 101, the mobile object information acquired by the mobile object information acquisition unit 102, and the concentration information acquired by the concentration information acquisition unit 104 are correlated with one another. In other words, the concentration-related information is generated as a set of information in which positioning information, mobile object information, and concentration information acquired simultaneously or within the same period are correlated with one another. For example, the related information generation unit 107 generates concentration-related information by correlating the mobile object information and concentration information for each interval at which the positioning information is acquired with the positioning information each time it acquires the positioning information. Furthermore, for example, the concentration-related information is generated in which a representative value of the positioning information for a specific period, a representative value of the mobile object information for the specific period, and a representative value of the driving concentration for the specific period are correlated with one another. Furthermore, for example, the concentration-related information is generated in which the positioning information indicating a specific section in which the vehicle is traveling, a representative value of the movement information for the specific section, and a representative value of the driving concentration for the specific section are correlated with one another. Possible representative values ​​include the average value, maximum value, minimum value, median value, etc. Here, the specific section may be specified with any start and end points, or may be specified in units of road links (hereinafter simply referred to as links) or sublinks.

[0027] With this configuration, the information processing device 100 generates concentration-related information in which the positioning information, mobile object information, and concentration level information are correlated with one another. The information processing device 100 may be provided in a vehicle, or some or all of its functions may be provided outside the vehicle. Furthermore, the information processing device 100 may be configured to generate concentration-related information in which the positioning information, mobile object information, and concentration level information are correlated with one another based on the positioning information, mobile object information, and concentration level information acquired from a plurality of vehicles. In other words, the information processing device 100 may be configured to generate first concentration-related information in which the positioning information, mobile object information, and concentration level information related to the driver of the first vehicle are correlated with one another based on the positioning information, mobile object information, and concentration level information related to the driver of the first vehicle acquired from a second vehicle different from the first vehicle, and to generate second concentration-related information in which the positioning information, mobile object information, and concentration level information related to the driver of the second vehicle are correlated with one another based on the positioning information, mobile object information, and concentration level information acquired from a second vehicle different from the first vehicle.

[0028] Next, the hardware configuration of the information processing device 100 will be described with reference to Figures 2 and 3. Figure 2 is a diagram showing an example of the hardware configuration of the information processing device 100, and Figure 3 is a diagram showing an example of the hardware configuration of the information processing device 100 that is different from that shown in Figure 2. For example, as shown in Figure 2, the information processing device 100 is a computer having a processor 100a, a memory 100b, and an I / O port 100c, and is configured so that the processor 100a reads and executes a program stored in the memory 100b.

[0029] 3, the information processing device 100 is a computer that has a processing circuit 100d, which is dedicated hardware, and an I / O port 100c, and executes a program. The processing circuit 100d is configured, for example, by a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each function of the information processing device 100 is realized by the processor 100a or the processing circuit 100d, which is dedicated hardware, executing a program. Note that the information processing device 100 may also have hardware other than those described above, such as a hardware timer.

[0030] Next, details of the processing performed by the information processing device 100 will be described with reference to Figures 1, 4, and 5. Figure 4 is a flowchart showing an example of the processing performed by the information processing device 100 according to the first embodiment. As shown in Figure 4, when the information processing device 100 starts the processing, it first acquires positioning information (step ST01). In this processing, the information processing device 100 acquires positioning information indicating the latitude and longitude at which the vehicle is located based on information from the positioning device 10, for example, at predetermined time intervals.

[0031] After performing the processing of step ST01, the information processing device 100 acquires mobile object information (step ST02). In this processing, the information processing device 100 acquires mobile object information during a positioning information acquisition period, which is, for example, the period from the last time positioning information was acquired until the positioning information was acquired in the processing of the immediately preceding step ST01. Specifically, the information processing device 100 acquires mobile object information indicating changes in the relative distance from the vehicle to one or more mobile objects around the vehicle as time-series data for each mobile object during the positioning information acquisition period. Furthermore, specifically, the information processing device 100 acquires mobile object information indicating changes in the relative distance and direction from the vehicle to the other vehicle closest to the vehicle as time-series data during the positioning information acquisition period. Furthermore, specifically, the information processing device 100 acquires mobile object information indicating the minimum value of the relative distance from the vehicle to the other vehicle closest to the vehicle traveling in a specific lane during the positioning information acquisition period. Specifically, the information processing device 100 acquires, during the positioning information acquisition period, moving body information indicating the average value of the relative distance from the vehicle to the other vehicle closest to the vehicle traveling on a specific lane.

[0032] After completing the process of step ST02, the information processing device 100 acquires image information of the driver (step ST05). In this process, the information processing device 100 acquires image information including at least a part of the face of the driver who is driving the vehicle while it is moving.

[0033] After performing the process of step ST05, the information processing device 100 acquires concentration level information (step ST06). In this process, the information processing device 100 acquires, for example, concentration level information indicating the driver's concentration level during the positioning information acquisition period. Specifically, the information processing device 100 acquires the concentration level information indicating changes in the driver's concentration level during the positioning information acquisition period as time-series data.

[0034] After performing the process of step ST06, the information processing device 100 generates concentration-related information (step ST08). In this process, the information processing device 100 generates concentration-related information in which the positioning information acquired in the process of step ST01 and the mobile object information and concentration information acquired during the positioning information acquisition period related to the positioning information are mutually associated.

[0035] After completing the process of step ST08, the information processing device 100 outputs concentration-related information (step ST09). In this process, the information processing device 100 outputs the generated concentration-related information to an external device communicatively connected to the information processing device 100. For example, in this process, the information processing device 100 outputs the generated concentration-related information to the storage device 60 and stores it in the storage device 60. After completing the process of step ST09, the information processing device 100 returns the process to step ST01 and acquires positioning information (step ST01).

[0036] 5 is a table showing an example of concentration-related information generated by the information processing device 100 according to embodiment 1. Specifically, FIG. 5 is a table showing an example of a data set consisting of multiple pieces of concentration-related information generated over multiple positioning information acquisition periods when the information processing device 100 according to embodiment 1 repeats the processes from step ST01 to step ST09 multiple times. As shown in FIG. 5 , for example, the concentration-related information generated by the information processing device 100 includes vehicle positions P(t1), P(t2), ..., P(tN) as positioning information at times t1, t2, ..., tN, driving concentration levels CL(t1), CL(t2), ..., CL(tN) as concentration information at times t1, t2, ..., tN, and mobile object information VN(t1), VN(t2), ..., VN(tN) indicating mobile object information at times t1, t2, ..., tN. 5, the concentration-related information defines the vehicle position as a specific position P(tn), but the concentration-related information may be created by expressing the vehicle position as a link corresponding to the vehicle position. That is, the position may be specified as a point or an area.

[0037] As described above, the information processing device 100 according to the first embodiment includes a positioning information acquisition unit 101 that acquires positioning information of the vehicle, a mobile body information acquisition unit 102 that acquires mobile body information indicating the relative positions of the vehicle and mobile bodies around the vehicle, a concentration information acquisition unit 104 that acquires concentration level information indicating the driving concentration level of the driver of the vehicle while it is in motion, and a related information generation unit 107 that generates concentration level related information that correlates the positioning information acquired by the positioning information acquisition unit 101, the mobile body information acquired by the mobile body information acquisition unit 102, and the concentration level information acquired by the concentration level information acquisition unit 104.

[0038] With this configuration, the information processing device 100 according to the first embodiment can generate concentration-related information in which the positioning information, the mobile object information, and the concentration information are mutually associated as information for predicting a change in the driver's concentration on driving based on, for example, the mobile object information and the concentration information. In other words, by analyzing the relationship between the positioning information, the mobile object information, and the concentration information based on the concentration-related information acquired when the driver drove in the past, it becomes possible to predict a change in the driver's concentration on driving based on the positioning information and the mobile object information.

[0039] In the first embodiment, the information processing device 100 is configured to acquire concentration level information indicating the driver's level of concentration on driving based on image information of the driver generated by the imaging device 30, but is not limited to this. The information processing device may also be configured to acquire concentration level information indicating the driver's level of concentration on driving while driving a moving vehicle based on information other than image information, and may be configured to acquire concentration level information based on information from a biosensor that acquires bioinformation such as the driver's heart rate, pulse rate, blood pressure, brain waves, respiration, body temperature, and blood oxygen concentration through contact or non-contact, or may be configured to acquire concentration level information based on image information and information from a biosensor.

[0040] Furthermore, in the first embodiment, the information processing device 100 is configured to output the generated concentration-related information to the external storage device 60, but is not limited to this. The information processing device only needs to be configured to generate concentration-related information, and may be configured to store the generated concentration-related information in a storage unit (not shown) that the information processing device has, or may be configured to output the generated concentration-related information to an external display device (not shown) in order to display the generated concentration-related information on the display device, or may be configured to output the generated concentration-related information to a trained model generation device in order to input the generated concentration-related information to an external trained model generation device (not shown) that generates a trained model.

[0041] Furthermore, in the first embodiment, the information processing device 100 is configured to generate concentration-related information in which positioning information, concentration level information, and mobile object information are correlated with one another, but this is not limiting. The information processing device is only required to generate concentration-related information that includes at least positioning information, concentration level information, and mobile object information and correlates these pieces of information with one another. For example, the concentration-related information generated by the information processing device may include information other than the positioning information, concentration level information, and mobile object information. For example, as shown in FIG. 5 , the concentration-related information generated by the information processing device may include a vehicle ID (identification) for identifying a vehicle related to the acquired positioning information, concentration level information, and mobile object information, and indices of alertness DL, inattentiveness LA, and absentmindedness VG used in calculating the driving concentration level. It may also include other information not shown in FIG. 5 , such as image information of the interior of the vehicle acquired by the image information acquisition unit 103 and information of the exterior of the vehicle acquired by the surrounding object detection device 20.

[0042] Specifically, the concentration-related information generated by the information processing device may include information indicating the lane of the road on which the vehicle is traveling, acquired by the positioning device 10 or the peripheral object detection device 20. The information processing device may be configured to generate concentration-related information in which the positioning information, concentration information, moving object information, and information indicating the lane of the road on which the vehicle is traveling are mutually associated. Generally, on roads with multiple lanes in the same direction, a driver's concentration on driving may vary depending on the lane they are traveling in. For example, a driver of a vehicle traveling in the passing lane is less likely to experience a decrease in their concentration on driving, while a driver of a vehicle traveling in the driving lane is more likely to experience a decrease in their concentration on driving. Furthermore, for example, in regions including Japan where vehicles travel in the left-hand lane of the road, a driver of a vehicle traveling in the leftmost lane of the road tends to pay less attention to the left side of the vehicle than a driver of a vehicle traveling in a lane to the right of the lane, and thus is more likely to experience a decrease in their concentration on driving. Therefore, by including information indicating the lane of the road on which the vehicle is traveling in the concentration-related information, it becomes possible to analyze in more detail the relationship between the degree of driving concentration and information other than the degree of driving concentration included in the concentration-related information.

[0043] 6 to 8, an information processing system 2 according to the second embodiment will be described. The information processing system 2 according to the second embodiment differs from the information processing system 1 according to the first embodiment in the configuration for the information processing device to generate concentration-related information based on other information in addition to positioning information, mobile object information, and concentration level information, and in the configuration for controlling the storage device 60 and the display device 80. However, the other configurations are the same, and the same configurations as those in the first embodiment are assigned the same names and symbols as those in the first embodiment, and descriptions thereof will be omitted.

[0044] Fig. 6 is a block diagram showing a schematic configuration of an information processing system 2 according to embodiment 2. As shown in Fig. 7, the information processing system 2 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, a database 50, a storage device 60, a display device 80, and an information processing device 200, which are connected wirelessly or by wire so as to be able to communicate with each other.

[0045] The database 50 stores various types of information used by the information processing device 200 for processing. For example, the database 50 stores date and time information related to the date and time and map information related to a map of the area where the vehicle travels. For example, the date and time information includes one or more of the following: information indicating the calendar, information indicating days of the week and holidays, information indicating the date and time of events held in the area where the vehicle travels, information regarding the climate and season of the area where the vehicle travels, information indicating the blooming period of specific flowers in the area where the vehicle travels, and meteorological information related to the weather associated with the date and time of the area where the vehicle travels. For example, the meteorological information includes information indicating the weather, hours of sunshine, amount of rainfall, fog occurrence, amount of snowfall, wind speed, wind direction, yellow sand occurrence, etc. For example, the database 50 stores meteorological information related to the weather measured or collected by organizations such as government agencies, NGOs, and private companies.

[0046] For example, the map information includes road information regarding the width of roads, the number of lanes, the direction of traffic, the shape of roads, the location of roads, the location of intersections, the type of road, etc., in the area in which the vehicle is traveling; facility information regarding facilities or buildings; and information about mountains, rivers, coastlines, and other natural features.

[0047] The display device 80 acquires information from the information processing device 200 and displays the acquired information as visual information to notify the viewer of the display device 80. For example, the display device 80 is configured with a liquid crystal display panel, an organic or inorganic EL (Electroluminescence) panel, a dot matrix display, an LED (Light Emitting Diode), or other display device. The display device 80 may have an input device (not shown) configured as a touch panel or a keyboard, or may have a speaker (not shown) capable of outputting sound based on information from the information processing device 200.

[0048] The information processing device 200 is a device for generating concentration-related information in which the positioning information, mobile object information, concentration information, map information, and date and time information are mutually associated, based on positioning information, mobile object information, and concentration level information indicating the driver's concentration level while driving the vehicle, and information from the database 50. The information processing device 200 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration level information acquisition unit 104, a map information acquisition unit 105, a date and time information acquisition unit 106, a related information generation unit 107, a memory control unit 108, and a display control unit 112.

[0049] The map information acquisition unit 105 acquires map information of the area in which the vehicle is traveling from the database 50. For example, the map information acquisition unit 105 acquires road information including information indicating a plurality of nodes representing the roads on which the vehicle is traveling and a plurality of links connecting the plurality of nodes, as the map information of the area in which the vehicle is traveling.

[0050] The date and time information acquisition unit 106 acquires date and time information relating to the date and time when the vehicle is traveling from the database 50. For example, the date and time information acquisition unit 106 acquires one or more pieces of information from the following: information indicating the date and time when the vehicle is traveling; information indicating the day of the week when the vehicle is traveling and whether it is a holiday; information indicating the season when the vehicle is traveling; information indicating events being held on the date and time when the vehicle is traveling; information indicating flowers that are blooming on the date and time when the vehicle is traveling; and weather information for the date and time when the vehicle is traveling.

[0051] The related information generation unit 107 generates concentration-related information in which the positioning information acquired by the positioning information acquisition unit 101, the mobile object information acquired by the mobile object information acquisition unit 102, and the concentration level information acquired by the concentration level information acquisition unit 104 are mutually associated. For example, the related information generation unit 107 according to the second embodiment generates concentration-related information in which the positioning information acquired by the positioning information acquisition unit 101, the mobile object information acquired by the mobile object information acquisition unit 102, the concentration level information acquired by the concentration level information acquisition unit 104, the map information acquired by the map information acquisition unit 105, and the date and time information acquired by the date and time information acquisition unit 106 are mutually associated.

[0052] The storage control unit 108 controls the storage device 60 by controlling information output from the information processing device 200 to the storage device 60. For example, the storage control unit 108 collectively outputs to the storage device 60 all the concentration-related information generated by the information processing device 200 during a specific period. Furthermore, for example, the storage control unit 108 extracts a portion of the concentration-related information generated by the information processing device 200 and outputs it to the storage device 60. Specifically, the storage control unit 108 extracts, from the concentration-related information generated by the information processing device 200, the concentration-related information in which the driving concentration level is less than a preset threshold value, and outputs it to the storage device 60.

[0053] The display control unit 112 controls the display device 80 by displaying information as visual information on the display device 80. For example, the display control unit 112 causes the display device 80 to display concentration-related information generated by the information processing device 200. Specifically, the display control unit 112 causes the display device 80 to display the concentration-related information generated by the information processing device 200, as shown in FIG. 5 . Furthermore, for example, the display control unit 112 causes the display device 80 to display each piece of information included in the concentration-related information generated by the information processing device 200 in association with each other. Specifically, the display control unit 112 causes the display device 80 to display each piece of information other than the map information included in the concentration-related information generated by the information processing device 200 in association with the map information. For example, the display control unit 112 causes the display device 80 to display the driver's concentration level on driving according to the vehicle's position by associating the vehicle's position indicated by the positioning information included in the concentration-related information generated by the information processing device 200 with the vehicle's position on the map indicated by the map information.

[0054] Furthermore, for example, the display control unit 112 displays information acquired from one or more of the positioning device 10, the surrounding object detection device 20, the imaging device 30, the database 50, and the storage device 60 on the display device 80. By displaying information on the display device 80, the display control unit 112 notifies the driver who is viewing the display device 80, other drivers or persons related to the vehicle, and drivers of other vehicles of the information. For example, persons related to the driver may be the driver's family members, the driver's supervisor, etc. Furthermore, persons related to the vehicle may be other drivers who drive the vehicle, the owner of the vehicle, the manager of the vehicle, etc. Details of the information displayed on the display device 80 by the display control unit 112 will be described later.

[0055] The hardware configuration of the information processing device 200 according to the second embodiment is similar to the hardware configuration of the information processing device 100 according to the first embodiment, and therefore a description thereof will be omitted.

[0056] Next, details of the processing performed by the information processing device 200 will be described with reference to Figures 6 to 8. Figure 7 is a flowchart showing an example of processing performed by the information processing device 200 according to embodiment 2. Note that part of the processing performed by the information processing device 200 according to embodiment 2 is similar to the processing performed by the information processing device 100 according to embodiment 1, and therefore, description of processing similar to embodiment 1 will be omitted. As shown in Figure 7, when the information processing device 200 starts processing, it first acquires positioning information (step ST01). After performing the processing of step ST01, the information processing device 200 acquires mobile object information (step ST02).

[0057] After completing the process of step ST02, the information processing device 200 acquires map information (step ST03). In this process, the information processing device 200 acquires, for example, road information around the vehicle position indicated by the positioning information acquired in step ST01.

[0058] After performing the process of step ST03, the information processing device 200 acquires date and time information (step ST04). In this process, the information processing device 200 acquires, for example, date and time information during the positioning information acquisition period related to the positioning information acquired in step ST01.

[0059] After performing the process of step ST04, the information processing device 200 acquires image information (step ST05).After performing the process of step ST05, the information processing device 200 acquires concentration level information (step ST06).

[0060] After performing the process of step ST06, the information processing device 200 determines whether the driving concentration level acquired in the immediately preceding step ST06 is equal to or greater than a preset threshold (step ST07). In this process, the information processing device 200 determines whether the information acquired in the processes of the immediately preceding steps ST01 to ST06 is information to be stored in the storage device 60 by the storage control unit 108. For example, the positioning information, mobile object information, and concentration level information acquired when the driving concentration level is equal to or greater than a preset threshold is less important as information than the positioning information, mobile object information, and concentration level information acquired when the driving concentration level is less than the preset threshold. Therefore, in the process of step ST07, the information processing device 200 determines whether the driving concentration level is equal to or greater than a preset threshold, thereby enabling the information acquired by the information processing device 200 to be extracted and stored in the storage device 60 with high importance.

[0061] If the driving concentration level is less than a preset threshold value in the process of step ST07 (NO in step ST07), the information processing device 200 generates concentration-level-related information (step ST08). In this process, the information processing device 200 generates concentration-level-related information in which, for example, the information acquired in the processes of the immediately preceding steps ST01 to ST06 is correlated with one another. Specifically, in this process, the information processing device 200 generates concentration-level-related information in which the positioning information acquired in the process of the immediately preceding step ST01, the mobile object information acquired during the positioning information acquisition period related to the positioning information, the concentration level information acquired during the positioning information acquisition period, map information including the position of the vehicle on a map indicated by the positioning information and road information of the road on which the vehicle is traveling, and date and time information during the positioning information acquisition period are correlated with one another.

[0062] After performing the process of step ST08, the information processing device 200 stores the concentration-related information in the storage device 60 (step ST10). In this process, the information processing device 200 outputs to the storage device 60 the concentration-related information that correlates the information obtained in the processes of the immediately preceding steps ST01 to ST06, based on the fact that the information obtained in the processes of the immediately preceding steps ST01 to ST05 is information associated with a driving concentration level that is less than a preset threshold value.

[0063] After performing the process of step ST10, the information processing device 200 causes the display device 80 to display the concentration-related information (step ST11). In this process, the information processing device 200 causes the display device 80 to display the concentration-related information, thereby visually notifying the viewer of the display device 80 of the concentration-related information.

[0064] In the process of step ST07, if the driving concentration level is equal to or greater than a preset threshold value (YES in step ST07), or if the process of step ST11 has been performed, the information processing device 200 determines whether the driver has finished driving the vehicle (step ST23). In this process, the information processing device 200 determines whether the driver has finished driving the vehicle, which is an example of a trigger for ending the process of generating concentration-related information. For example, in this process, the information processing device 200 determines whether the driver has finished driving the vehicle based on the state of the vehicle's power switch, the state of the ignition switch, the position of the shift lever, etc.

[0065] In the process of step ST23, if driving has not ended (NO in step ST23), the information processing device 200 returns the process to step ST01 and acquires positioning information (step ST01). In this way, by repeating the processes of steps ST01 to ST23, the information processing device 200 can accumulate time-series data of concentration-related information for a specific period during driving. In the process of step ST23, if driving has ended (YES in step ST23), the information processing device 200 ends the process.

[0066] 8 is a diagram showing an example of an image that information processing device 200 according to embodiment 2 displays on display device 80 based on concentration-related information. As shown in Fig. 8, information processing device 200 displays, for example, information indicating the change in the level of concentration on driving over time when the driver previously drove a vehicle and the position of the vehicle on a map corresponding to the change in the level of concentration on driving over time, based on time-series data of concentration-related information generated in the past, on display device 80. Specifically, information processing device 200 displays a graph showing the change in the level of concentration on driving over time, a symbol M1 indicating the position of the vehicle on a map shown by the positioning information and the map information, and a road R1 on which the vehicle is traveling.

[0067] For example, FIG. 8 shows that, when a vehicle traveling on road R1 was driven in the past, the driver's driving concentration level fell below a preset threshold value CL(tn) in the section from point P(tn-) on road R1 where the vehicle was traveling at time tn- to point P(tn+) where the vehicle was traveling at time tn+. For example, the storage control unit 108 generates concentration-related information for the period from time tn- to time tn+ and stores it in the storage device 60. Note that the information processing device may be configured to generate concentration-related information for a period including any time in the period from time tn- to time tn+ and store it in the storage device 60. For example, the information processing device may be configured to generate concentration-related information at time tn, which is intermediate between time tn- and time tn+, and store it in the storage device 60. Alternatively, the information processing device may be configured to generate concentration-related information for a period longer than the period including the period from time tn- to time tn+ and store it in the storage device 60. The information processing device may also be configured to generate concentration-related information for all periods during which the vehicle is traveling, and to have the storage control unit extract, from the generated concentration-related information, information in which the driver's driving concentration is below a preset threshold, and store the information in the storage device. With this configuration, the information processing device extracts information of high importance from the acquired information and stores it in the storage device 60, thereby reducing the amount of communication with the storage device and reducing the storage capacity required for the storage device.

[0068] 8 is displayed on the display device 80, if a moving operation to move the symbol M1 is performed on an input device (not shown), the information processing device 200 causes the display device 80 to display an image of the symbol M1 being moved to a position corresponding to the moving operation. Also, if a selection operation to select the symbol M1 is performed on an input device (not shown) while the information shown in FIG. 8 is displayed on the display device 80, the information processing device 200 causes the display device 80 to display other information included in the concentration-related information, such as positioning information corresponding to the position of the symbol M1 on the map, mobile object information, values ​​of each index of the degree of concentration on driving, image information of the interior of the vehicle, date and time information, etc.

[0069] By displaying information on display device 80 in this manner, for example, a viewer of display device 80 can, by viewing the information shown in Figure 8 displayed on display device 80, predict that when a vehicle travels on road R1 in the future, the driver's level of concentration on driving may fall below a threshold value in the section between point P(tn-) and point P(tn+).

[0070] As described above, the information processing device 200 according to the second embodiment includes the map information acquisition unit 105 that acquires map information of the area in which the vehicle is traveling, and the related information generation unit 107 is configured to generate concentration-related information that mutually associates the positioning information acquired by the positioning information acquisition unit 101, the mobile object information acquired by the mobile object information acquisition unit 102, the concentration information acquired by the concentration information acquisition unit 104, and the map information acquired by the map information acquisition unit 105. With this configuration, the information processing device 200 according to the second embodiment can predict changes in the level of concentration on driving based not only on the positioning information and the mobile object information but also on the position of the vehicle on the map.

[0071] Furthermore, the information processing device 200 according to the second embodiment is configured to include a display control unit 112 that controls the display device 80 to display information included in the concentration-related information generated by the related information generation unit 107 in association with the map information acquired by the map information acquisition unit 105. With this configuration, the information processing device 200 according to the second embodiment can predict changes in the level of concentration on driving based not only on the positioning information and the mobile object information but also on the visual position of the vehicle on the map displayed on the display device 80.

[0072] Furthermore, the information processing device 200 according to the second embodiment includes a date and time information acquisition unit 106 that acquires date and time information related to the date and time when the vehicle is traveling, and the related information generation unit 107 is configured to generate concentration-related information that mutually associates the positioning information acquired by the positioning information acquisition unit 101, the mobile object information acquired by the mobile object information acquisition unit 102, the concentration information acquired by the concentration information acquisition unit 104, and the date and time information acquired by the date and time information acquisition unit 106. With this configuration, the information processing device 200 according to the second embodiment can predict changes in the level of concentration on driving based not only on the positioning information and the mobile object information, but also on the date and time information related to the date and time when the vehicle was driven. Furthermore, the information processing device 200 according to embodiment 2 generates concentration correlations in which positioning information, mobile object information, concentration information, and date and time information are mutually associated. For example, by including in the date and time information information on dates and times when distracted driving is likely to occur, such as information indicating the date and time of a fireworks display, information indicating the date and time of a festival, information indicating the date and time of cherry blossom blooming, and information indicating the best time to view autumn leaves, it becomes possible to perform a more detailed analysis of changes in concentration information based on the concentration correlation information.

[0073] 9 to 12, an information processing system 3 according to the third embodiment will be described. The information processing system 3 according to the third embodiment differs from the information processing system 1 according to the first embodiment in the configuration of the output destination of information from the information processing device, but the other configurations are the same. The same names and symbols as those in the first embodiment are used for the same configurations as those in the first embodiment, and the description thereof will be omitted.

[0074] Fig. 9 is a block diagram showing a schematic configuration of an information processing system 3 according to embodiment 3. As shown in Fig. 9, the information processing system 3 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, an information processing device 100, a map information generation device 900, and a database 50, which are connected wirelessly or by wire so as to be able to communicate with each other.

[0075] The information processing device 100 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration level information acquisition unit 104, a related information generation unit 107, and a storage control unit 108. Configured in this manner, the information processing device 100 generates concentration level related information in which the positioning information, the mobile object information, and the concentration level information are associated with each other, and outputs the generated concentration level related information to the map information generation device 900.

[0076] The map information generating device 900 includes a related information acquiring unit 901, a map information acquiring unit 902, a date and time information acquiring unit 903, a storage control unit 904, a map information generating unit 905, and a storage unit 906 that stores information used in each process performed by the map information generating device 900 and information generated by the map information generating device 900. For example, the map information generating device 900 is configured by a server computer provided outside the vehicle, and is connected to an information processing device 100 provided in the vehicle via a communication network NT1 so that they can communicate with each other.

[0077] The related information acquisition unit 901 acquires concentration-related information from the information processing device 100. The related information acquisition unit 901 may be configured to acquire concentration-related information every time the information processing device 100 generates the concentration-related information, or may be configured to collectively acquire concentration-related information generated by the information processing device 100 over a specific period. For example, the related information acquisition unit 901 acquires, from the information processing device 100, the concentration-related information generated every time the information processing device 100 acquires positioning information. Furthermore, for example, the related information acquisition unit 901 collectively acquires, from the information processing device 100, the concentration-related information generated by the information processing device 100 over a period from the start to the end of vehicle driving, after the driving ends. Note that the related information acquisition unit 901 may be configured to acquire, for each vehicle, concentration-related information in which the positioning information, mobile object information, and concentration information acquired by the information processing device 100 from multiple vehicles are associated with each other, based on the positioning information, mobile object information, and concentration information acquired from these vehicles, or may be configured to acquire concentration-related information from multiple information processing devices connected via a communication network.

[0078] The map information acquisition unit 902 acquires, from the database 50, map information of the area in which the vehicle related to the concentration-related information acquired by the related information acquisition unit 901 is traveling. The function of the map information acquisition unit 902 is similar to that of the map information acquisition unit 105 according to the second embodiment, and therefore a description thereof will be omitted.

[0079] The date and time information acquisition unit 903 acquires, from the database 50, date and time information relating to the date and time when the vehicle related to the concentration level related information acquired by the related information acquisition unit 901 is traveling. The function of the date and time information acquisition unit 903 is similar to that of the date and time information acquisition unit 106 according to the second embodiment, and therefore a description thereof will be omitted.

[0080] The storage control unit 904 controls the information to be stored in the storage unit 906. For example, the storage control unit 904 stores information acquired from the information processing device 100 in the storage unit 906. Furthermore, for example, the storage control unit 904 stores information acquired from the database 50 in the storage unit 906.

[0081] The map information generation unit 905 generates map-related information in which information included in the concentration level-related information generated by the information processing device 100 and the map information acquired by the map information acquisition unit 902 are associated with each other, based on the information stored in the storage unit 906. Note that the map-related information generated by the map information generation unit 905 is similar to the concentration level-related information generated by the information processing device 200 according to Embodiment 2 in which multiple pieces of information including concentration level information are associated with each other, and therefore a description thereof will be omitted. The map information generation unit 905 stores the generated map-related information in the storage unit 906 using the storage control unit 904. Note that the map information generation device may include a display control unit that displays the generated map-related information on a display device (not shown), or the information processing device may include a display control unit that displays the map-related information generated by the map information generation device on a display device (not shown).

[0082] The hardware configuration of the information processing device 100 according to embodiment 3 and the hardware configuration of the map information generating device 900 according to embodiment 3 are the same as the hardware configuration of the information processing device 100 according to embodiment 1, and therefore will not be described here.

[0083] Next, details of the processing performed by the map information generation device 900 will be described with reference to Figures 9 to 12. Figure 10 is a flowchart showing an example of processing performed by the map information generation device 900 according to embodiment 3. Note that the processing performed by the information processing device 100 according to embodiment 3 is similar to the processing performed by the information processing device 100 according to embodiment 1, and therefore description thereof will be omitted. As shown in Figure 10, when the map information generation device 900 starts processing, it first acquires concentration level-related information (step ST31). In this processing, the map information generation device 900 acquires the concentration level-related information from the information processing device 100, for example, via the communication network NT1.

[0084] After completing the process of step ST31, the map information generating device 900 acquires map information (step ST32). In this process, the map information generating device 900 refers to information stored in the database 50 and acquires, from the database 50, map information of the area in which the vehicle associated with the concentration-related information acquired in the process of the immediately preceding step ST31 is traveling. For example, the map information acquired in this process includes lane information for identifying the lanes in which the vehicle and other vehicles are traveling.

[0085] After completing the process of step ST32, the map information generating device 900 acquires date and time information (step ST33). In this process, the map information generating device 900 refers to the information stored in the database 50 and acquires, from the database 50, date and time information relating to the date and time when the vehicle related to the concentration level-related information acquired in the process of the immediately preceding step ST31 is traveling.

[0086] After completing the process of step ST33, the map information generating device 900 associates the concentration level-related information, map information, and date and time information and stores them in the storage unit 906 (step ST34). In this process, the map information generating device 900 associates the concentration level-related information acquired from the information processing device 100 in the process of the immediately preceding step ST31, the map information acquired from the database 50 in the process of the immediately preceding step ST32, and the date and time information acquired from the database 50 in the process of the immediately preceding step ST33 with each other and stores them in the storage unit 906.

[0087] After performing the process of step ST34, the map information generating device 900 generates map-related information as map information based on the acquired information (step ST35). In this process, the map information generating device 900 generates map-related information in which the concentration-related information acquired from the information processing device 100 in the process of the immediately preceding step ST31, the map information acquired from the database 50 in the process of the immediately preceding step ST32, and the date and time information acquired from the database 50 in the process of the immediately preceding step ST33 are mutually associated.

[0088] After completing step ST35, the map information generation device 900 returns to step ST31 and acquires concentration-related information (step ST31). The map information generation device 900 may be configured to display the map-related information generated in step ST35 on a display device (not shown). Alternatively, the information processing system may be configured so that an information processing device acquires the map-related information generated by the map information generation device 900 in step ST35 and displays the map-related information on a display device. While FIG. 10 illustrates an example in which steps ST31 to ST35 are sequentially looped and executed as a single process, steps ST31 to ST34 may be sequentially looped, with a first process collecting information in a storage unit and a second process generating map information from the information collected in the storage unit at a predetermined timing. In this case, the process of ST35 can be executed independently, such as daily, weekly, monthly, or at the operator's command, without synchronizing with the timing at which the concentration-related information is acquired in step ST31. Furthermore, when generating map information, the concentration-related information acquired within a single link may be used to determine a representative value of the concentration-related information within that link using a statistical method, and the map information may be generated as a link attribute. Alternatively, the representative value of multiple pieces of concentration-related information within the same link may be set as an attribute value. Figure 11 shows an example of map information generated in step 35 based on the information storing the concentration-related information shown in Figure 5. Note that for links where the concentration level has not decreased, the link attribute may simply be defined as a link where the concentration level has not decreased, thereby reducing the amount of data. In Figure 11, the value of CL(Ln) may be set to 1.

[0089] 12A, 12B, and 12C are diagrams illustrating an example of an image that the information processing system 3 according to the third embodiment displays on a display device based on map-related information. For example, as shown in FIG. 12A, the information processing system 3 displays on a display device a road R1 on which a vehicle is traveling, concentration-lowering position symbols T1, T2, T3, T4, and T5 indicating positions where the driver's driving concentration level has decreased, and a cursor C1, based on the map-related information. Furthermore, for example, when an input operation is performed on an input device (not shown) to place the cursor C1 over a specific concentration-lowering position symbol among the concentration-lowering position symbols T1 to T5 while the image shown in FIG. 12A is displayed on the display device, the information processing system 3 changes the display mode of the specific concentration-lowering position symbol to a different color from the other concentration-lowering position symbols. For example, at this time, the information processing system 3 changes the color of the specific concentration-lowering position symbol to a color different from the other concentration-lowering position symbols.

[0090] 12A is displayed on the display device, and an input operation is performed on the input device to move the cursor C1 over a specific one of the concentration-lowering position symbols T1 to T5, the information processing system 3 causes the display device to display image information of the driver generated by the imaging device 30 while the vehicle is positioned at a position corresponding to the concentration-lowering position symbol, as shown in FIG. 12B. At this time, the information processing system 3 may be configured to cause the display device to display information related to the driver's concentration level together with the image information of the driver. Specifically, at this time, the information processing system 3 may be configured to cause the display device to display explanatory information J1 shown in FIG. 12B for explaining the state in which the driver's concentration level has decreased, together with the image information of the driver.

[0091] 12A is displayed on the display device, and an input operation is performed on the input device to move the cursor C1 over a specific one of the concentration level reduction position symbols T1 to T5, the information processing system 3 causes the display device to display moving object information as shown in Fig. 12C. The moving object information shown in Fig. 12C includes a vehicle V1 as the subject vehicle, lane markings K1, K2, and K3 of the road on which the vehicle V1 is traveling and lanes S1 and S2 defined by the lane markings K1 to K3, another vehicle V2 as a preceding vehicle on the lane S1 on which the vehicle V1 is traveling, and a distance L1 indicating the relative positions of the vehicle V1 and the other vehicle V2.

[0092] As described above, in the information processing system 3 according to the third embodiment, the map information acquired by the map information acquisition unit is configured to include lane information for identifying the lanes that the vehicle and other vehicles are traveling in. With this configuration, the information processing system 3 according to the third embodiment can predict changes in the level of concentration on driving based on the mobile object information indicating the lanes that the vehicle and other vehicles are traveling in.

[0093] In addition, the map information generating device 900 may be connected to the information processing device 100 so as to be able to communicate directly with them without going through the communication network NT1, the information processing device 100 and the map information generating device 900 may both be provided in the same vehicle, or some or all of the functions of the information processing device 100 and the map information generating device 900 may be provided outside the vehicle.

[0094] 13 and 14, an information processing system 4 according to the fourth embodiment will be described. The information processing system 3 according to the fourth embodiment differs from the information processing system 1 according to the first embodiment in the configuration for calculating the similarity between past concentration-related information and new concentration-related information, and the configuration for controlling the storage device 60 and the display device 80, but the other configurations are the same. The same components as those in the first embodiment are given the same names and symbols as those in the first embodiment, and the description thereof will be omitted.

[0095] Fig. 13 is a block diagram showing a schematic configuration of an information processing system 4 according to embodiment 4. As shown in Fig. 13, the information processing system 4 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, a storage device 60, a display device 80, and an information processing device 400, which are connected wirelessly or by wire so as to be able to communicate with each other. Note that the details of the storage device 60 and the display device 80 are the same as those of the information processing system 2 according to embodiment 2, and therefore will not be described here.

[0096] The information processing device 400 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration level information acquisition unit 104, a related information generation unit 107, a memory control unit 108, a similarity calculation unit 109, and a display control unit 112.

[0097] The similarity calculation unit 109 calculates the similarity between the specific information including the new positioning information and mobile body information acquired by the positioning information acquisition unit 101 and the mobile body information acquisition unit 102, and information corresponding to the specific information included in the concentration level related information already generated by the related information generation unit 107. In other words, the similarity calculation unit 109 calculates the similarity between the specific information including the new positioning information and mobile body information acquired by the positioning information acquisition unit 101 and the mobile body information acquisition unit 102, and information corresponding to the specific information included in the concentration level related information previously generated by the related information generation unit 107.

[0098] For example, the similarity calculation unit 109 calculates the similarity between new positioning information and mobile body information acquired by the positioning information acquisition unit 101 and the mobile body information acquisition unit 102, and mobile body information included in the concentration level related information previously generated by the related information generation unit 107, which corresponds to the new positioning information acquired by the positioning information acquisition unit 101. For example, the similarity calculation unit 109 calculates the similarity between the past mobile body information and the new mobile body information as a value ranging from 0, which is the lowest similarity, to 1, which is the highest similarity.

[0099] With this configuration, the similarity calculation unit 109 can, for example, extract new positioning information and mobile object information and past concentration-related information whose positioning information corresponds to the new positioning information and mobile object information and whose mobile object information is similar. Note that the similarity calculation unit 109 may be configured to calculate the similarity by comparing the new mobile object information with the past mobile object information, or may be configured to calculate the similarity using a trained model that learns based on multiple pieces of mobile object information and calculates, based on input of new mobile object information, the similarity between the new mobile object information and mobile object information included in previously generated concentration-related information.

[0100] The details of the storage control unit 108 and the display control unit 112 are the same as those of the storage control unit 108 and the display control unit 112 according to embodiment 2, and therefore will not be described again. The hardware configuration of the information processing device 400 according to embodiment 4 is the same as that of the information processing device 100 according to embodiment 1, and therefore will not be described again.

[0101] Next, details of the processing performed by the information processing device 400 will be described with reference to Figures 13 and 14. Figure 14 is a flowchart showing an example of the processing performed by the information processing device 400 according to embodiment 4. Note that part of the processing performed by the information processing device 400 according to embodiment 4 is similar to the processing performed by the information processing device 100 according to embodiment 1 and the processing performed by the information processing device 200 according to embodiment 2, and therefore, a description of the processing similar to embodiment 1 or embodiment 2 will be omitted.

[0102] As shown in FIG. 14 , when the information processing device 400 starts processing, it first acquires positioning information (step ST01). After performing the processing of step ST01, the information processing device 400 acquires mobile object information (step ST02). After performing the processing of step ST02, the information processing device 400 acquires image information (step ST05). After performing the processing of step ST05, the information processing device 400 acquires concentration level information (step ST06). After performing the processing of step ST06, the information processing device 400 generates concentration level-related information (step ST08). After performing the processing of step ST08, the information processing device 400 stores the concentration level-related information in the storage device 60 (step ST10). After performing the processing of step ST10, the information processing device 400 displays the concentration level-related information on the display device 80 (step ST11).

[0103] After performing the process of step ST11, the information processing device 400 acquires past concentration-related information (step ST12). In this process, the information processing device 400 refers to the information stored in the storage device 60 and acquires the concentration-related information that the information processing device 400 previously generated and stored in the storage device 60.

[0104] After performing the processing of step ST12, the information processing device 400 calculates the similarity between the previously acquired mobile object information and the newly acquired mobile object information (step ST15). In this processing, the information processing device 400 extracts, from the plurality of pieces of past concentration-related information acquired in the processing of the immediately preceding step ST12, concentration-related information including positioning information corresponding to the new positioning information acquired in the processing of the immediately preceding step ST01, and calculates the similarity between the mobile object information of the extracted concentration-related information and the new mobile object information by the similarity calculation unit 109. In other words, the information processing device 400 extracts, from the plurality of pieces of past concentration-related information acquired in the processing of the immediately preceding step ST12, the concentration-related information corresponding to the new positioning information acquired in the processing of the immediately preceding step ST01, and calculates the similarity between the mobile object information included in the extracted concentration-related information and the new mobile object information by the similarity calculation unit 109.

[0105] For example, in the processing of step ST15, the similarity calculation unit 109 first extracts, from the past concentration-related information acquired in the processing of the immediately preceding step ST12, concentration-related information including positioning information corresponding to the new positioning information, concentration-related information including positioning information in which the distance along the road from the vehicle indicated by the new positioning information is less than a preset threshold value. Such a threshold value is, for example, preset to 250 m. Furthermore, if the new positioning information (current position) is p(t), using the expression in FIG. 5, P(tn) is extracted such that p(t) -250 m < P(k) < p(t) + 250 m. In other words, the similarity is calculated for the new positioning information over the following 500 m: P(k) -250 m < p(t) < P(k) + 250 m. Here, P(k) is replaced with P(tn) in FIG. 5. The threshold value is not limited to 250 m, and may be set by the user as desired. It may also be set to a distance that would be reached in one minute if the driver were to drive at the current driving speed. It may also be variable depending on the primary concentration indicator that indicates a decrease in driving concentration. For example, a 10-minute distance may be used for decreased alertness, a 1-minute distance for distracted driving, and a 3-minute distance for absentmindedness.

[0106] Next, the similarity calculation unit 109 calculates the similarity between the new mobile object information acquired in the processing of the immediately preceding step ST02 and the past mobile object information included in the extracted past concentration level-related information. For example, the similarity calculation unit 109 calculates the similarity between the new mobile object information and the past mobile object information included in the extracted past concentration level-related information by the following procedure.

[0107] First, if concentration-related information including positioning information in which the distance along the road from the vehicle indicated by the new positioning information is less than a preset threshold is not stored in the storage device 60, the similarity calculation unit 109 calculates the similarity as 0 and terminates the calculation of the similarity. Furthermore, if a plurality of pieces of concentration-related information including positioning information in which the distance along the road from the vehicle indicated by the new positioning information is less than a preset threshold is stored in the storage device 60, the similarity calculation unit 109 also refers to the concentration-related information including the positioning information in which the distance along the road from the vehicle indicated by the new positioning information is the shortest as target information, which is concentration-related information that is the target of similarity calculation.

[0108] Next, the similarity calculation unit 109 calculates the similarity between the new mobile object information and the past mobile object information included in the target information. In other words, the similarity calculation unit 109 calculates the similarity S using the following formula (1): S = f(vn(t), VN(k)) (1) For convenience of explanation, new mobile object information is represented by lowercase vn, and stored past mobile object information is represented by uppercase VN. Note that f is a function for calculating the similarity S, vn(t) is new mobile object information at time t, i.e., mobile object information at the current location, and VN(k) is past mobile object information at time k included in the target information. The similarity calculation unit 109 calculates the similarity S, for example, by comparing mobile object information vn(t) at time t with mobile object information VN(k) (stored mobile object information) at time k, which is earlier than time t.

[0109] Furthermore, the similarity calculation unit 109 calculates similarity Sm, which is the average value of similarities in a specific period TL, by comparing new mobile object information in a specific period TL including time t with mobile object information in a period including time k before time t and having the same length as the period TL, using, for example, the following formula (2). Calculating similarity S in this manner makes it possible to improve the reliability of the calculation result compared to calculating instantaneous similarity S. Sm = (1 / TL) · ∫ {vn(t) - VN(k)} dt ... (2) In formula (1), the integration range is t = (t - TL) to t. TL is, for example, 2 minutes.

[0110] Furthermore, the similarity calculation unit 109 may be configured to discretely calculate the similarity Sm using the following formula (3): S=(1 / N)Σ{vn(t−n·T)−VN(k)} (3) In formula (2), n=0 to (N−1). T is, for example, 30 seconds, and N is, for example, 5 times. By calculating the similarity using formula (3), the similarity calculation unit 109 averages the similarity every 30 seconds over a period of 2 minutes.

[0111] Furthermore, for example, the similarity calculation unit 109 calculates the similarity S between new moving object information and past moving object information based on the moving object information indicating the distance between the vehicle and other vehicles, using the following mathematical formula (4): S=1-|x(t)-X(k)| / 250 (4) where x(t) is the new moving object information indicating the distance between the vehicle and other vehicles at time t, and X(k) is the moving object information at time k prior to time t. Furthermore, in mathematical formula (4), for example, if the distance between the vehicle and other vehicles is 250 m or more, the similarity calculation unit 109 performs the calculation using |x(t)-X(k)|=250.

[0112] Furthermore, for example, the similarity calculation unit 109 calculates the similarity S between the new moving body information and the past moving body information using the following formula (5) so that the closer the distance between the vehicle and another vehicle is, the greater the similarity becomes: S=1−{|x(t)−X(k)| / 250} ２ ...(5) Note that the above-mentioned formulas (1) to (5) are specific examples of calculation formulas used by the similarity calculation unit 109 to calculate the similarity, and the similarity calculation unit 109 may be configured to calculate the similarity using an algorithm other than these formulas (1) to (5).

[0113] After performing the process of step ST15, the information processing device 400 determines whether the similarity is equal to or greater than a preset threshold value (step ST18). In this process, when the similarity is calculated as a value between 0 and 1, for example, the information processing device 400 determines whether the similarity calculated in the process of the immediately preceding step ST15 is equal to or greater than a preset threshold value of 0.5. In this process, the information processing device 400 determines whether the acquired new positioning information and mobile object information are information requiring attention that may reduce the driver's concentration on driving.

[0114] If the similarity is equal to or greater than a predetermined threshold in the process of step ST18 (YES in step ST18), the information processing device 400 displays, on the display device 80, warning information for alerting viewers of the display device 80 about the driver's level of concentration on driving (step ST20). In this process, the display control unit 112 determines, for example, based on the fact that the similarity was equal to or greater than a predetermined threshold in the process of the immediately preceding step ST18, to cause the display device 80 to display, as warning information, a character string indicating that the driver's level of concentration on driving may decrease when the driving concentration level included in the concentration-related information serving as the target information is less than the predetermined threshold, thereby determining to notify the driver of the possibility of a decrease in the driver's level of concentration on driving by the display device 80 as a warning device. Note that in the fourth embodiment, the display device 80 constitutes a warning device that issues a warning, and the display control unit 112 constitutes a warning unit that determines to notify the driver by the warning device.

[0115] For example, in this process, the information processing device 400 displays, as warning information, a character string saying "Lots of distracted driving ahead" on the display device 80. Also, for example, in this process, the information processing device 400 displays, as warning information, a character string saying "Beware of a decrease in driving concentration level" on the display device 80. Also, for example, in this process, the information processing device 400 displays, as warning information, map information including the location where the vehicle is traveling and an image showing a location on the map where the driving concentration level may decrease, such as the image shown in FIG. 12A , on the display device 80. Also, for example, in this process, the information processing device 400 displays, as warning information, an image showing map information including the location where the vehicle is traveling and a graph showing a change in driving concentration level predicted based on new positioning information and mobile object information, such as the image shown in FIG. 8 , on the display device 80.

[0116] In addition, if the similarity is equal to or greater than a predetermined threshold value in the processing of step ST18 (YES in step ST18), the information processing device may be configured to notify viewers of the display device 80 or other relevant parties of warning information by voice, or may be configured to output information to a driving assistance device that provides driving assistance for the vehicle to change the content of the driving assistance so as to reduce the burden on the driver.

[0117] In the process of step ST18, if the similarity is less than a preset threshold value (NO in step ST18), and in the process of step ST20, the information processing device 400 determines whether or not driving has ended (step ST23). In the process of step ST23, if driving has not ended (NO in step ST23), the information processing device 400 returns the process to step ST01 and acquires positioning information (step ST01). In addition, in the process of step ST23, if driving has ended (YES in step ST23), the information processing device 400 ends the process.

[0118] As described above, the information processing device 400 according to the fourth embodiment includes the similarity calculation unit 109 that calculates the similarity between the new positioning information and mobile body information acquired by the positioning information acquisition unit 101 and the mobile body information acquisition unit 102, and information corresponding to the new positioning information included in the concentration-related information already generated by the related information generation unit 107. With this configuration, the information processing device 400 can predict a change in the driving concentration level of a driver of a vehicle traveling at a position indicated in the new positioning information, based on the new positioning information and mobile body information and the past concentration-related information.

[0119] Furthermore, the information processing device 400 according to the fourth embodiment includes a display control unit 112 that determines whether to issue a notification on the display device 80 based on the similarity calculated by the similarity calculation unit 109 and the concentration level information included in the concentration level-related information corresponding to the new positioning information. With this configuration, the information processing device 400 can notify the driver of a vehicle traveling at a position indicated by the new positioning information that the driver's concentration level on driving may be decreasing, for example, when the driving concentration level indicated by the concentration level information included in the concentration level-related information corresponding to the new positioning information is less than a preset threshold value.

[0120] In the fourth embodiment, the information processing device 400 is configured to calculate the similarity between new mobile object information and mobile object information included in past concentration-related information, but is not limited to this. The information processing device may be configured to calculate the similarity between specific information including new positioning information and mobile object information acquired by the positioning information acquisition unit and the mobile object information acquisition unit, and information corresponding to specific information included in concentration-related information already generated by the related information generation unit. For example, the information processing device may be configured to calculate the similarity between specific information including new positioning information and mobile object information acquired by the positioning information acquisition unit and the mobile object information acquisition unit, and date and time information according to the second embodiment, and the positioning information, mobile object information, and the date and time information included in the concentration-related information already generated by the related information generation unit. This configuration allows the information processing device to improve the reliability of the similarity compared to when calculating the similarity based only on mobile object information.

[0121] In the fourth embodiment, the information processing device may be configured to generate concentration-related information based on the positioning information, mobile object information, and concentration level information acquired from multiple vehicles, in which the information is correlated with one another. With this configuration, even when a specific vehicle travels on a road it has never traveled on before, it is possible to predict a change in the driver's concentration level based on new positioning information and mobile object information acquired by the vehicle's travel and concentration level-related information generated by the past travels of other vehicles. In steps ST12 and ST15 of the fourth embodiment, a method for acquiring past concentration-related information such as that shown in FIG. 5 and calculating the similarity between past (stored) mobile object information and current mobile object information at a current (new) position has been described. However, the similarity may also be calculated by acquiring concentration-related information from map information stored with concentration-related information such as that shown in FIG. 10a, which was created in the third embodiment. In this case, in the process of step ST15, the similarity calculation unit 109 first extracts, from the map information storing the concentration-related information acquired in the process of the immediately preceding step ST12, concentration-related information associated with links whose distance along the road from the vehicle indicated by the new positioning information is less than a preset threshold. Similarly, when the location of the concentration-related information in FIG. 5 is expressed as an area, the similarity is calculated in accordance with the method using FIG. 11. Furthermore, when the location of the concentration-related information is expressed as an area or a link, the preset threshold may be 0 m instead of 250 m. Furthermore, when the map information storing the concentration-related information is used for similarity determination and new concentration-related information is not generated, steps ST5, ST6, ST8, and ST10 are not necessarily required, and the components that execute these steps are not necessarily required. This corresponds to the host vehicle acquiring and using a database created based on the driving concentration-related information of other vehicles.

[0122] 15 to 17, an information processing system 5 according to embodiment 5 will be described. The information processing system 5 according to embodiment 5 is different from the information processing system 4 according to embodiment 4 in that the information processing device includes a control information generating unit instead of a display control unit, and the operation control device is controlled based on information generated by the control information generating unit. However, other configurations are the same, and the same configurations as those of embodiment 4 are assigned the same names and symbols as those of embodiment 4, and descriptions thereof will be omitted.

[0123] Fig. 15 is a block diagram showing a schematic configuration of an information processing system 5 according to embodiment 5. As shown in Fig. 15, the information processing system 5 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, a driving control device 40, a storage device 60, and an information processing device 500, which are connected wirelessly or by wire so as to be able to communicate with each other.

[0124] The driving control device 40 controls at least one of the speed and direction of the vehicle. For example, the driving control device 40 controls at least one of the speed and direction of the vehicle to provide driving assistance to the driver using a Lane Keep Assist System (LKAS), Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), etc. Note that the driving control device 40 is only required to be configured to control the vehicle at least equivalent to Level 1 autonomous driving, and may be configured to enable fully autonomous driving of the vehicle. Furthermore, in addition to controlling the speed and direction of the vehicle, the driving control device 40 may be configured to provide driving assistance to the driver by outputting audio from a speaker (not shown), or may be configured to display images or text on a display device (not shown) to provide driving assistance to the driver.

[0125] The information processing device 500 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration level information acquisition unit 104, a related information generation unit 107, a memory control unit 108, a similarity calculation unit 109, and a control information generation unit 113.

[0126] The control information generation unit 113 generates control information for changing the control content by the driving control device 40 based on the similarity calculated by the similarity calculation unit 109 and the concentration level information included in the previously generated concentration level-related information that corresponds to the new positioning information acquired by the positioning information acquisition unit 101. For example, if the similarity calculated by the similarity calculation unit 109 is equal to or greater than a preset threshold value and if the concentration level information included in the previously generated concentration level-related information that corresponds to the new positioning information acquired by the positioning information acquisition unit 101 is less than the preset threshold value, the control information generation unit 113 generates a command value for the driving control device 40 as control information for changing the control content of the vehicle by the driving control device 40 so as to reduce the driving burden on the driver.

[0127] The hardware configuration of the information processing device 500 according to the fifth embodiment is similar to the hardware configuration of the information processing device 100 according to the first embodiment, and therefore a description thereof will be omitted.

[0128] Next, details of the processing performed by the information processing device 500 will be described with reference to Figures 15 to 17. Figure 16 is a flowchart showing an example of the processing performed by the information processing device 500 according to embodiment 5. Note that part of the processing performed by the information processing device 500 according to embodiment 5 is similar to the processing performed by the information processing device 400 according to embodiment 4, and therefore, a description of the processing similar to embodiment 4 will be omitted.

[0129] As shown in FIG. 16 , when the information processing device 500 starts processing, it first acquires positioning information (step ST01). After performing the processing of step ST01, the information processing device 500 acquires mobile object information (step ST02). After performing the processing of step ST02, the information processing device 500 acquires image information (step ST05). After performing the processing of step ST05, the information processing device 500 acquires concentration level information (step ST06). After performing the processing of step ST06, the information processing device 500 generates concentration level-related information (step ST08). After performing the processing of step ST08, the information processing device 500 stores the concentration level-related information in the storage device 60 (step ST10). After performing the processing of step ST10, the information processing device 500 acquires past concentration level-related information (step ST12). After performing the processing of step ST12, the information processing device 500 calculates the similarity between previously acquired mobile object information and newly acquired mobile object information (step ST15). After performing the process of step ST15, the information processing device 500 determines whether the similarity is equal to or greater than a preset threshold value (step ST18).

[0130] If the similarity is equal to or greater than a predetermined threshold in the processing of step ST18 (YES in step ST18), the information processing device 500 generates control information for changing the vehicle control content by the driving control device 40 (step ST21). In this processing, the control information generation unit 113 generates, for example, control information for the driving control device 40, for changing the control content by the driving control device 40 to control content corresponding to the driving concentration level included in the concentration-related information when the driving concentration level included in the concentration-related information as the target information is less than a predetermined threshold, based on the similarity being equal to or greater than a predetermined threshold in the processing of the immediately preceding step ST18. Specifically, the control information generation unit 113 generates, as control information for the driving control device 40, control information for changing the control content by the driving control device 40 to control content that reduces the driver's driving burden in accordance with the driving concentration level included in the concentration-related information when the driving concentration level included in the concentration-related information as the target information is less than a predetermined threshold, based on the similarity being equal to or greater than a predetermined threshold in the processing of the immediately preceding step ST18.

[0131] The information processing device 500 may be configured to generate control information such that the higher the similarity calculated in the processing of step ST15, the higher the degree of driving assistance provided by the driving control device 40. For example, the information processing device 500 may be configured to generate control information such that the higher the similarity calculated in the processing of step ST15, the higher the degree of driving assistance provided by the driving control device 40, such as by adding audio output for driving assistance, increasing the autonomous driving level, or shifting the parameters of the autonomous driving control to a safer side. Also, for example, the higher the similarity calculated in the processing of step ST15, the stronger the LKAS, so that when the vehicle approaches a lane boundary, the LKAS may be activated earlier, the vehicle may increase the distance between vehicles, the vehicle may decelerate earlier when the preceding vehicle decelerates and the distance between vehicles becomes shorter, or the information processing device 500 may output an earlier contact warning, an earlier alertness warning, or the like by voice.

[0132] After completing the process of step ST21, the information processing device 500 outputs control information (step ST22). In this process, the information processing device 500 outputs, to the operation control device 40, control information generated in the process of the immediately preceding step ST21 for changing the control content to be controlled by the operation control device 40 to control content corresponding to the driving concentration level included in the concentration level-related information.

[0133] FIG. 17 is a diagram showing a section where driving assistance is provided by the information processing device 500 according to embodiment 5. In FIG. 17 , T6, T7, T8, T9, and T10 are concentration-level drop position symbols indicating positions where a drop in driving concentration level is predicted. For example, as shown in FIG. 17 , when a vehicle is traveling on road R1, if the similarity S(t) between new moving object information and past moving object information included in the concentration-related information, including concentration-level information indicating that the driver's driving concentration level is below a preset threshold, is higher than a threshold value Sth during the period from time t1 to time t2 between time t1 and time t3 (case 1), the information processing device 500 outputs control information from the information processing device 500 to the driving control device 40 to provide driving assistance to the driver during the period from time t1 to time t2 during which the similarity S(t) is higher than the threshold value Sth during the period during which the vehicle is traveling on link Lk, which is the section from node Nk corresponding to vehicle position P1 at time t1 to node Nk+1 corresponding to vehicle position P3 at time t3. The period during which driving assistance is provided is not limited to this, and driving assistance may be provided until the end of the period during which the vehicle is traveling on link Lk, or until a specific time after time t2 (for example, two minutes after time t2).

[0134] Furthermore, for example, as shown in FIG. 17, when a vehicle is traveling on road R1, if the similarity S(t) between new mobile object information and past mobile object information included in the concentration-related information, which includes concentration information in which the driver's driving concentration level is below a predetermined threshold value, is lower than the threshold value Sth during the period from time t1 to time t3 (case 2), the information processing device 500 will not output control information to the driving control device 40 during the period in which the vehicle is traveling on link Lk, and no driving assistance will be provided to the driver.

[0135] The information processing device may be configured to change the content of the driving assistance provided by the driving control device 40 according to the predicted level of concentration on driving. For example, the information processing device may be configured to change the content of the driving assistance provided by the driving control device 40 so that the lower the predicted level of concentration on driving, the more the burden on the driver is reduced. For example, when the predicted level of concentration on driving is less than a first threshold value and equal to or greater than a second threshold value that is smaller than the first threshold value, the information processing device may be configured to cause the driving control device 40 to provide driving assistance by outputting audio, and when the predicted level of concentration on driving is less than the second threshold value, the information processing device may be configured to cause the driving control device 40 to provide driving assistance by controlling at least one of the speed and direction of the vehicle.

[0136] In addition, the information processing device may be configured to set the start timing of the driving assistance provided by the driving control device 40 so that the lower the predicted level of driving concentration, the earlier the start timing of the driving assistance corresponding to the level of driving concentration; or, if it is detected based on image information acquired by the image information acquisition unit 103 that the driver's line of sight is not directed toward other vehicles in the vicinity, the information processing device may be configured to set the start timing of the driving assistance provided by the driving control device 40 so that the start timing of the driving assistance corresponding to the level of driving concentration is earlier; or, if it is detected that the driver's line of sight is not directed toward other vehicles in the vicinity, the information processing device may be configured to cause the driving control device 40 to provide driving assistance that controls at least one of the vehicle's speed and direction so that the distance to the other vehicles does not become too small.

[0137] In the process of step ST18, if the similarity is less than a preset threshold value (NO in step ST18), and in the process of step ST22, the information processing device 500 determines whether or not driving has ended (step ST23). In the process of step ST23, if driving has not ended (NO in step ST23), the information processing device 500 returns the process to step ST01 and acquires positioning information (step ST01). In addition, in the process of step ST23, if driving has ended (YES in step ST23), the information processing device 500 ends the process.

[0138] As described above, the information processing device 500 according to the fifth embodiment includes the control information generation unit 113, which generates control information for changing the control content of the driving control device 40, which controls at least one of the vehicle's speed and direction, based on the similarity calculated by the similarity calculation unit 109 and the concentration level information included in the concentration level-related information corresponding to the new positioning information. With this configuration, the information processing device 500 according to the fifth embodiment can provide driving assistance according to a prediction of the driving concentration level of the driver of the vehicle traveling at the location indicated in the new positioning information, based on the new positioning information, mobile object information, and past concentration level-related information. Furthermore, by not providing driving assistance when the similarity is below a threshold, unnecessary driving assistance can be prevented. Note that, as in the fourth embodiment, if map information storing concentration level-related information is used to determine the similarity and new concentration level-related information is not generated, steps ST5, ST6, ST8, and ST10 are not necessarily required. This corresponds to the host vehicle acquiring and using a database created based on the driving concentration level-related information of other vehicles.

[0139] Sixth Embodiment Next, an information processing system 6 according to a sixth embodiment will be described with reference to Figures 18 and 19. The information processing system 6 according to the sixth embodiment differs from the information processing system 5 according to the fifth embodiment in the configuration for including information other than positioning information, mobile object information, and concentration level information in the concentration level-related information generated by the information processing device, and in the configuration for generating a trained model based on the concentration level-related information, but the other configurations are the same. Therefore, the same names and symbols as those in the fifth embodiment will be used and descriptions thereof will be omitted.

[0140] Fig. 18 is a block diagram showing a schematic configuration of an information processing system 6 according to embodiment 6. As shown in Fig. 18, the information processing system 6 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, a driving control device 40, a database 50, a storage device 60, a trained model generation device 70, a display device 80, and an information processing device 600, which are connected wirelessly or by wire so as to be able to communicate with each other. Note that the details of the database 50 and the display device 80 are the same as those of the information processing system 2 according to embodiment 2, and therefore will not be described here.

[0141] The trained model generation device 70 learns based on multiple pieces of mobile object information from the information processing device 600 and generates a trained model that calculates the similarity between mobile object information included in already generated concentration-related information and the new mobile object information based on input of new mobile object information. In other words, the trained model generation device 70 learns based on multiple pieces of mobile object information from the information processing device 600 and generates a trained model with the new mobile object information as an input variable and the similarity between the new mobile object information and the mobile object information included in already generated concentration-related information as an objective variable. For example, the trained model generation device 70 acquires multiple pieces of concentration-related information from the information processing device 600 and generates a trained model by learning based on mobile object information extracted from the acquired concentration-related information.

[0142] Furthermore, for example, the trained model generation device 70 learns through unsupervised learning that classifies multiple pieces of mobile object information from the information processing device 600 based on their mutual similarities, and generates a trained model that calculates the similarity between the mobile object information included in the already generated concentration-related information and the new mobile object information based on the input of new mobile object information. Various known algorithms can be used as the trained model algorithm. The trained model generation device 70 may generate a trained model based on mobile object information acquired by the driving of a specific vehicle, or may generate a trained model based on mobile object information acquired by the driving of multiple vehicles.

[0143] Furthermore, the trained model generation device 70 may be configured to generate a single trained model with fixed parameters, or, if the information acquired from the information processing device 600 is information acquired by multiple vehicles traveling and is information that can identify each vehicle, the trained model may be generated so that different parameters are set for each vehicle, or, if the information acquired from the information processing device 600 is information acquired by multiple drivers driving vehicles and is information that can identify each driver, the trained model may be generated so that different parameters are set for each driver. Furthermore, the trained model generation device 70 may be configured to be communicably connected to the storage device 60 and to store the generated trained model in the storage device 60.

[0144] The information processing device 600 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration level information acquisition unit 104, a map information acquisition unit 105, a date and time information acquisition unit 106, a related information generation unit 107, a memory control unit 108, a similarity calculation unit 110, a display control unit 112, and a control information generation unit 113.

[0145] The similarity calculation unit 110 acquires the trained model generated by the trained model generation device 70 from the trained model generation device 70, and calculates the similarity between the mobile body information included in the concentration-related information that has already been generated and the new mobile body information based on the input of new mobile body information to the acquired trained model.

[0146] The details of the map information acquisition unit 105 and the date and time information acquisition unit 106 are the same as those of the information processing device 200 according to embodiment 2, and therefore will not be described again. The hardware configuration of the information processing device 600 according to embodiment 6 is the same as that of the information processing device 100 according to embodiment 1, and therefore will not be described again.

[0147] Next, details of the processing performed by the information processing device 600 will be described with reference to Figures 18 to 19. Figure 19 is a flowchart showing an example of processing performed by the information processing device according to embodiment 6 for outputting concentration-related information to the trained model generation device. Note that part of the processing shown in Figure 19 performed by the information processing device 600 according to embodiment 6 is similar to the processing performed by the information processing device 200 according to embodiment 2, and therefore, a description of processing similar to that of embodiment 2 will be omitted.

[0148] As shown in FIG. 19 , when the information processing device 600 starts processing, it first acquires positioning information (step ST01). After performing the processing of step ST01, the information processing device 600 acquires mobile object information (step ST02). After performing the processing of step ST02, the information processing device 600 acquires map information (step ST03). After performing the processing of step ST03, the information processing device 600 acquires date and time information (step ST04). After performing the processing of step ST04, the information processing device 600 acquires image information (step ST05). After performing the processing of step ST05, the information processing device 600 acquires concentration level information (step ST06). After performing the processing of step ST06, the information processing device 600 generates concentration level-related information (step ST08).

[0149] After performing the processing of step ST08, the information processing device 600 inputs concentration-related information to the trained model generation device 70 (step ST13). In this processing, the information processing device 600 outputs the concentration-related information generated in the processing of the immediately preceding step ST08 to the trained model generation device 70. After performing the processing of step ST13, the information processing device 600 returns the processing to step ST01. In this way, by repeating the processing from step ST01 to step ST13, multiple pieces of mobile object information are input to the trained model generation device 70, and a trained model is generated.

[0150] 20 is a flowchart showing an example of processing for generating vehicle control information using a trained model performed by the information processing device 600 according to embodiment 6. Note that part of the processing shown in FIG. 20 performed by the information processing device 600 according to embodiment 6 is similar to the processing performed by the information processing device 500 according to embodiment 5, and therefore, a description of the processing similar to embodiment 5 will be omitted.

[0151] As shown in FIG. 20 , when the information processing device 600 starts processing, it first acquires positioning information (step ST01). After performing the processing of step ST01, the information processing device 600 acquires mobile object information (step ST02). After performing the processing of step ST02, the information processing device 600 acquires map information (step ST03). After performing the processing of step ST03, the information processing device 600 acquires date and time information (step ST04). After performing the processing of step ST04, the information processing device 600 acquires image information (step ST05). After performing the processing of step ST05, the information processing device 600 acquires concentration level information (step ST06). After performing the processing of step ST06, the information processing device 600 generates concentration level-related information (step ST08). After performing the processing of step ST08, the information processing device 600 stores the concentration level-related information in the storage device 60 (step ST10).

[0152] After performing the process of step ST10, the information processing device 600 acquires a trained model (step ST14). In this process, the information processing device 600 acquires from the trained model generation device 70 a trained model that learns based on multiple pieces of mobile object information from the information processing device 600 and calculates, based on input of new mobile object information, the similarity between mobile object information included in the already generated concentration-related information and the new mobile object information.

[0153] After performing the process of step ST14, the information processing device 600 calculates the similarity between the previously acquired mobile body information and the newly acquired mobile body information (step ST16). In this process, the information processing device 600 inputs the new mobile body information into the trained model acquired in the process of step ST14, thereby calculating the similarity between the previously acquired mobile body information and the newly acquired mobile body information using the trained model.

[0154] After performing the process of step ST16, the information processing device 600 determines whether the similarity is equal to or greater than a preset threshold (step ST18). If the similarity is equal to or greater than the preset threshold in the process of step ST18 (YES in step ST18), the information processing device 600 generates control information (step ST21). After performing the process of step ST21, the information processing device 600 outputs the control information (step ST22). If the similarity is less than the preset threshold in the process of step ST18 (NO in step ST18) and after performing the process of step ST22, the information processing device 600 determines whether driving has ended (step ST23). If the driving has not ended in the process of step ST23 (NO in step ST23), the information processing device 600 returns to step ST01 and acquires positioning information (step ST01). If the driving has ended in the process of step ST23 (YES in step ST23), the information processing device 600 ends the process.

[0155] As described above, in the information processing device 600 according to the sixth embodiment, the similarity calculation unit 110 is configured to calculate, based on input of new mobile object information, the similarity between mobile object information included in already generated concentration-related information and the new mobile object information, using a trained model trained based on multiple pieces of mobile object information. With this configuration, the information processing device 600 can calculate the similarity between past mobile object information and new mobile object information, even if the content of the mobile object information is difficult to compare among multiple pieces of mobile object information.

[0156] In the sixth embodiment, the information processing device 600 is configured to use a trained model trained based on multiple pieces of mobile object information to calculate the similarity between new mobile object information and mobile object information included in previously generated concentration-related information based on input of new mobile object information. However, the trained model used to calculate the similarity is not limited to one that calculates the similarity only between mobile object information. For example, the trained model generation device may be configured to learn based on specific information, including mobile object information, consisting of multiple pieces of information included in the concentration-related information, and generate a trained model that calculates the similarity between past specific information and new specific information. The information processing device may be configured to calculate the similarity between past specific information and new specific information using the trained model thus generated. For example, information included in the specific information may include, in addition to mobile object information, date and time information acquired by a date and time information acquisition unit and lane information acquired by a positioning device or a surrounding object detection device. By calculating the similarity between specific information using such a trained model, the information processing device can improve the reliability of the similarity.

[0157] Seventh Embodiment Next, an information processing system 7 according to a seventh embodiment will be described with reference to Fig. 21 and Fig. 21. The information processing system 7 according to the seventh embodiment differs from the information processing system 6 according to the sixth embodiment in the configuration for estimating the driver's concentration on driving based on the positioning information and the mobile object information, but the other configurations are the same. The same configurations as those in the sixth embodiment are given the same names and symbols as those in the sixth embodiment, and the description thereof will be omitted.

[0158] Fig. 21 is a block diagram showing a schematic configuration of an information processing system 7 according to embodiment 7. As shown in Fig. 21 , the information processing system 7 includes a positioning device 10, a surrounding object detection device 20, an imaging device 30, a driving control device 40, a database 50, a storage device 60, a trained model generation device 71, a display device 80, and an information processing device 700, which are connected wirelessly or by wire so as to be able to communicate with each other.

[0159] The trained model generation device 71 learns based on multiple pieces of concentration-related information acquired from the information processing device 700, and generates a trained model that estimates the driver's level of concentration on driving based on input of positioning information acquired by the positioning information acquisition unit 101 and mobile object information acquired by the mobile object information acquisition unit 102. In other words, the trained model generation device 71 learns based on multiple data sets each including positioning information, mobile object information, and concentration information indicating the driver's level of concentration on driving, and generates a trained model that estimates the driver's level of concentration on driving based on input of the positioning information acquired by the positioning information acquisition unit 101 and the mobile object information acquired by the mobile object information acquisition unit 102. For example, the trained model generation device 71 generates a trained model by supervised learning in which the positioning information and mobile object information are input variables and the driver's level of concentration on driving is an objective variable.

[0160] The trained model generation device 71 may be configured to generate a single trained model with fixed parameters. Alternatively, if the information acquired from the information processing device 700 is information acquired from the driving of multiple vehicles and is information that can identify each vehicle, the trained model may be generated so that different parameters are set for each vehicle. Alternatively, if the information acquired from the information processing device 700 is information acquired from the driving of vehicles by multiple drivers and is information that can identify each driver, the trained model may be generated so that different parameters are set for each driver. Generally, even if the positioning information and mobile object information are the same, the impact on the level of concentration on driving may differ for each driver. For example, if the trained model generation device 71 generates a trained model such that different parameters are set for each driver, it becomes possible to generate a trained model based on changes in the level of concentration on driving for each driver, thereby improving the reliability of the estimated results of the level of concentration on driving.

[0161] The information processing device 700 includes a positioning information acquisition unit 101, a mobile object information acquisition unit 102, an image information acquisition unit 103, a concentration level information acquisition unit 104, a map information acquisition unit 105, a date and time information acquisition unit 106, a related information generation unit 107, a memory control unit 108, a concentration level estimation unit 111, a display control unit 112, and a control information generation unit 113.

[0162] The concentration level estimation unit 111 acquires the learned model generated by the learned model generation device 71 from the learned model generation device 71, and estimates the driver's concentration level while driving based on the input of new positioning information and mobile object information for the acquired learned model.

[0163] The hardware configuration of the information processing device 700 according to the seventh embodiment is similar to the hardware configuration of the information processing device 100 according to the first embodiment, and therefore a description thereof will be omitted.

[0164] Next, details of the processing performed by the information processing device 700 will be described with reference to Figures 21 and 22. Figure 22 is a flowchart showing an example of processing performed by the information processing device according to embodiment 7. Note that part of the processing performed by the information processing device 700 according to embodiment 7 is similar to the processing performed by the information processing device 600 according to embodiment 6 and shown in Figure 20, and therefore description of processing similar to that of embodiment 6 will be omitted.

[0165] As shown in FIG. 22 , when the information processing device 700 starts processing, it first acquires positioning information (step ST01). After performing the processing of step ST01, the information processing device 700 acquires mobile object information (step ST02). After performing the processing of step ST02, the information processing device 700 acquires map information (step ST03). After performing the processing of step ST03, the information processing device 700 acquires date and time information (step ST04). After performing the processing of step ST04, the information processing device 700 acquires image information (step ST05). After performing the processing of step ST05, the information processing device 700 acquires concentration level information (step ST06). After performing the processing of step ST06, the information processing device 700 generates concentration level-related information (step ST08). After performing the processing of step ST08, the information processing device 700 stores the concentration level-related information in the storage device 60 (step ST10).

[0166] After performing the process of step ST10, the information processing device 700 acquires a trained model (step ST14). In this process, the information processing device 700 acquires a trained model that learns based on the plurality of concentration-related information acquired from the information processing device 700 from the trained model generation device 71 and estimates the driver's concentration level on driving based on the input of the positioning information acquired by the positioning information acquisition unit 101 and the mobile object information acquired by the mobile object information acquisition unit 102.

[0167] After performing the process of step ST14, the information processing device 700 estimates the driver's concentration level on driving (step ST17). In this process, the information processing device 700 inputs new positioning information and mobile object information into the trained model acquired in the process of step ST14, thereby calculating the driver's concentration level on driving using the trained model.

[0168] After performing step ST17, the information processing device 700 determines whether the driving concentration level is equal to or greater than a preset threshold (step ST19). If the driving concentration level is less than the preset threshold (NO in step ST19), the information processing device 700 generates control information (step ST21). After performing step ST21, the information processing device 700 outputs control information (step ST22). If the driving concentration level is equal to or greater than the preset threshold (YES in step ST19) and if step ST22 is performed, the information processing device 700 determines whether driving has ended (step ST23). If the driving has not ended (NO in step ST23), the information processing device 700 returns to step ST01 and acquires positioning information (step ST01). If the driving has ended (YES in step ST23), the information processing device 700 ends the process.

[0169] As described above, the information processing device 700 according to the seventh embodiment includes a positioning information acquisition unit 101 that acquires positioning information of the vehicle, a mobile object information acquisition unit 102 that acquires mobile object information indicating the relative positions of the vehicle and mobile objects around the vehicle, and a concentration level estimation unit 111 that estimates the driver's concentration level on driving based on input of the positioning information acquired by the positioning information acquisition unit 101 and the mobile object information acquired by the mobile object information acquisition unit 102, using a trained model trained based on multiple data sets each including the positioning information, the mobile object information, and concentration level information indicating the driver's concentration level on driving. With this configuration, the information processing device 700 is able to estimate the driver's concentration level on driving based on previously acquired positioning information, mobile object information, and concentration level information.

[0170] In the seventh embodiment, the information processing device 700 is configured to estimate the driver's level of concentration on driving based on positioning information and mobile object information. However, the trained model used to estimate the level of concentration on driving is not limited to one that estimates the level of concentration on driving based only on positioning information and mobile object information. For example, the trained model generation device may be configured to learn based on specific information, including positioning information, mobile object information, and concentration level information, which is composed of multiple pieces of information included in the concentration level-related information, and generate a trained model that estimates the level of concentration on driving based on information other than the concentration level information included in the specific information. The information processing device may be configured to estimate the driver's level of concentration on driving using the trained model thus generated. For example, information included in the specific information may include, in addition to mobile object information, date and time information acquired by a date and time information acquisition unit, and lane information acquired by a positioning device or a surrounding object detection device. By using such a trained model to estimate the driver's level of concentration on driving, the information processing device can improve the reliability of the estimation result.

[0171] In any of the above-mentioned embodiments, the information processing device may include some or all of the other components of the information processing system, or some of the components may be provided in an external device that is communicatively connected to the information processing device, or may be communicatively connected to the other components of the information processing system via other devices, computers, or communication networks not shown.

[0172] In addition, the present disclosure allows for free combination of the respective embodiments, modification of any of the components of the respective embodiments, or omission of any of the components of the respective embodiments.

[0173] The information processing device of the present disclosure generates concentration-related information in which positioning information, mobile object information, and concentration information are mutually associated, and can, for example, predict changes in a driver's driving concentration based on the mobile object information and concentration information, and use the prediction results to provide vehicle driving assistance.

[0174] 1 Information processing system, 2 Information processing system, 3 Information processing system, 4 Information processing system, 5 Information processing system, 6 Information processing system, 7 Information processing system, 10 Positioning device, 20 Peripheral object detection device, 30 Imaging device, 40 Driving control device, 50 Database, 60 Storage device, 70 Trained model generation device, 71 Trained model generation device, 80 Display device, 101 Positioning information acquisition unit, 102 Mobile object information acquisition unit, 103 Image information acquisition unit, 104 Concentration level information acquisition unit, 105 Map information acquisition unit, 106 Date and time information acquisition unit, 107 Related information generation unit, 108 Memory control unit, 109 Similarity calculation unit, 110 Similarity calculation unit, 111 Concentration level estimation unit, 112 Display control unit, 113 Control information generation unit, 200 Information processing device, 400 Information processing device, 500 Information processing device, 600 Information processing device, 700 information processing device, 900 map information generation device, 901 related information acquisition unit, 902 map information acquisition unit, 903 date and time information acquisition unit, 904 memory control unit, 905 map information generation unit, 906 memory unit, C1 cursor, CL driving concentration level, DL alertness level, J1 explanatory information, K1 lane marking, K2 lane marking, K3 lane marking, L1 distance, LA inattentiveness level, Lk link, M1 symbol, NT1 communication network, Nk node, P vehicle position, R1 road, S similarity, S1 lane, S2 lane, Sm similarity, Sth threshold, TL period, V1 vehicle, V2 vehicle, VG absentmindedness level, VN mobile object information.

Claims

1. An information processing device comprising: a positioning information acquisition unit that acquires positioning information of a vehicle; a mobile body information acquisition unit that acquires mobile body information indicating the relative position of the vehicle and mobile bodies around the vehicle; a concentration information acquisition unit that acquires concentration level information that indicates the level of concentration of a driver driving the vehicle while it is in motion; and a related information generation unit that generates concentration level related information that correlates the positioning information acquired by the positioning information acquisition unit, the mobile body information acquired by the mobile body information acquisition unit, and the concentration level information acquired by the concentration level information acquisition unit.

2. An information processing device as described in claim 1, characterized in that it is provided with a map information acquisition unit that acquires map information of the area in which the vehicle is traveling, and the related information generation unit generates concentration-related information that correlates the positioning information acquired by the positioning information acquisition unit, the mobile object information acquired by the mobile object information acquisition unit, the concentration information acquired by the concentration information acquisition unit, and the map information acquired by the map information acquisition unit.

3. The information processing device according to claim 2, wherein the map information acquired by the map information acquisition unit includes lane information for identifying the lanes on which the vehicle and other vehicles are traveling.

4. An information processing device as described in claim 2, characterized in that it is provided with a display control unit that controls the display device to display information contained in the concentration-related information generated by the related information generation unit in association with the map information based on the map information acquired by the map information acquisition unit.

5. An information processing device as described in claim 1, characterized in that it is provided with a date and time information acquisition unit that acquires date and time information regarding the date and time when the vehicle is traveling, and the related information generation unit generates concentration related information that correlates the positioning information acquired by the positioning information acquisition unit, the mobile object information acquired by the mobile object information acquisition unit, the concentration information acquired by the concentration information acquisition unit, and the date and time information acquired by the date and time information acquisition unit.

6. An information processing device as described in claim 1, characterized in that it is provided with a similarity calculation unit that calculates the similarity between specific information including new positioning information and mobile body information acquired by the positioning information acquisition unit and the mobile body information acquisition unit, and information corresponding to the specific information included in the concentration level related information already generated by the related information generation unit.

7. The information processing device described in claim 6, characterized in that the similarity calculation unit uses a trained model trained based on multiple pieces of mobile object information to calculate the similarity between the mobile object information included in the already generated concentration-related information and the new mobile object information based on the input of the new mobile object information.

8. An information processing device as described in claim 6, characterized in that it is provided with an alarm unit that determines whether to issue an alarm using an alarm device based on the similarity calculated by the similarity calculation unit and the concentration level information included in the concentration level related information corresponding to the new positioning information.

9. An information processing device as described in claim 6, characterized in that it is provided with a control information generation unit that generates control information for changing the control content by a driving control device that controls at least one of the speed and direction of the vehicle based on the similarity calculated by the similarity calculation unit and the concentration information included in the concentration related information corresponding to the new positioning information.

10. An information processing device according to any one of claims 1 to 9, characterized in that the related information generation unit generates concentration-related information during a period in which the driver's concentration on driving is less than a preset threshold value.

11. An information processing device as claimed in any one of claims 1 to 9, characterized in that it is provided with a memory control unit that extracts, from the concentration-related information generated by the related information generation unit, concentration-related information in which the driver's driving concentration level is below a predetermined threshold value, and stores the information in a memory device.

12. An information processing device comprising: a positioning information acquisition unit that acquires positioning information of a vehicle; a mobile object information acquisition unit that acquires mobile object information indicating the relative position of the vehicle and mobile objects around the vehicle; and a concentration level estimation unit that estimates the driver's level of concentration on driving based on input of the positioning information acquired by the positioning information acquisition unit and the mobile object information acquired by the mobile object information acquisition unit, using a trained model trained based on multiple data sets each including positioning information, mobile object information, and concentration level information indicating the driver's level of concentration on driving.

13. An information processing method performed by a device having a positioning information acquisition unit, a mobile object information acquisition unit, a concentration information acquisition unit, and a related information generation unit, comprising the steps of: the positioning information acquisition unit acquiring vehicle positioning information; the mobile object information acquisition unit acquiring mobile object information indicating the relative position of the vehicle and mobile objects around the vehicle; the concentration information acquisition unit acquiring concentration information indicating the driving concentration level of a driver operating the vehicle while in motion; and the related information generation unit generating concentration-related information in which the positioning information acquired by the positioning information acquisition unit, the mobile object information acquired by the mobile object information acquisition unit, and the concentration information acquired by the concentration information acquisition unit are mutually associated.