Information processing device, information processing method, and computer-readable medium

Abstract

Provided is an information processing device capable of appropriately ascertaining a behavior that is characteristic of the vehicle of a driver having a specific attribute. This information processing device acquires an image obtained by imaging one or more points at which a vehicle can travel. The information processing device identifies an attribute of the driver of the vehicle shown in the image. The information processing device acquires, for individual types of behavior, travel data indicating the behavior of the vehicle at the point with respect to each of the attributes of at least two drivers. The information processing device uses a predetermined assessment criterion to assess, for each type of behavior, whether the degree of deviation between the overall travel data and travel data relating to a vehicle of one or more drivers having a predetermined first attribute is high. The information processing device identifies the type of behavior assessed to have a high degree of deviation as a first behavior that is characteristic of the driving of the driver having the first attribute. The information processing device performs a process for presenting the identified first behavior.

Description

Information Processing Apparatus, Information Processing Method, and Computer-Readable Medium

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and a computer-readable medium.

[0002] Patent Document 1 discloses a traffic management system that manages the driving status of a specific vehicle on a road. The system according to Patent Document 1 recognizes a specific vehicle, which is a vehicle with a predetermined driver identifier, based on a captured image, and manages the driving status of the specific vehicle for each section of the road.

[0003] Japanese Patent Application Laid-Open No. 2020-119397

[0004] In the technology according to Patent Document 1, it merely manages the driving status of a specific vehicle. Therefore, in the technology according to Patent Document 1, there is a possibility that the characteristic behavior of a vehicle of a driver with a specific attribute cannot be specified. Therefore, it is desirable to appropriately grasp the characteristic behavior of a vehicle of a driver with a specific attribute.

[0005] An object of the present disclosure is to solve such problems, and to provide an information processing apparatus, an information processing method, and a program capable of appropriately grasping the characteristic behavior of a vehicle of a driver with a specific attribute.

[0006] The information processing apparatus according to the present disclosure includes an image acquisition unit that acquires an image obtained by photographing one or more points where a vehicle can travel, an attribute specification unit that specifies an attribute of a driver of the vehicle reflected in the image, a travel data acquisition unit that acquires travel data indicating the behavior of the vehicle at the point for each type of behavior for each of at least two driver attributes, a deviation degree determination unit that determines whether a deviation degree between overall travel data, which is travel data regarding vehicles of drivers of at least two attributes, and travel data regarding vehicles of one or more drivers of a predetermined first attribute is large using a predetermined determination criterion, a behavior specification unit that specifies, as a first behavior, which is a characteristic behavior in the driving of a driver of the first attribute, the type of behavior for which the deviation degree is determined to be large, and a presentation unit that performs a process for presenting the specified first behavior.

[0007] The information processing method according to this disclosure includes: an image acquisition means that acquires images obtained by photographing one or more locations where a vehicle can travel; an image acquisition means that identifies the attributes of the driver of the vehicle shown in the image; for each of at least two driver attributes, acquires driving data indicating the behavior of the vehicle at the location for each type of behavior; for each type of behavior, determines, using predetermined criteria, whether the degree of discrepancy between the overall driving data, which is the driving data for the vehicles of at least two drivers with certain attributes, and the driving data for the vehicles of one or more drivers with a predetermined first attribute is large; the type of behavior for which the degree of discrepancy is determined to be large is identified as a first behavior, which is a characteristic behavior of the driving of the driver with the first attribute; and processing to present the identified first behavior.

[0008] The program according to this disclosure causes a computer to execute the following steps: an image acquisition means for acquiring images obtained by photographing one or more locations where a vehicle can travel; a step of identifying the attributes of the driver of the vehicle shown in the image; a step of acquiring driving data indicating the behavior of the vehicle at the location for each type of behavior for each of at least two driver attributes; a step of determining, using predetermined criteria, whether the degree of discrepancy is large between the overall driving data, which is the driving data for the vehicles of at least two drivers with attributes, and the driving data for the vehicles of one or more drivers with a predetermined first attribute; a step of identifying the type of behavior for which the degree of discrepancy is determined to be large as a first behavior, which is a characteristic behavior of the driving of the driver with the first attribute; and a step of processing to present the identified first behavior.

[0009] According to this disclosure, it is possible to provide an information processing device, an information processing method, and a program that can appropriately grasp the characteristic behavior of a vehicle driven by a driver with specific attributes.

[0010] This is a diagram showing the configuration of the information processing device related to this disclosure. This is a flowchart showing the information processing method executed by the information processing device related to this disclosure. This is a diagram illustrating the configuration of the information presentation system related to this disclosure. This is a diagram illustrating the hardware configuration of the information processing device related to this disclosure. This is a diagram illustrating the functional configuration of the information processing device related to this disclosure. This is a diagram for explaining the process of acquiring driving data by the information processing device related to this disclosure. This is a diagram illustrating behavioral data acquired by the driving data acquisition unit related to this disclosure. This is a diagram illustrating driving data acquired by the driving data acquisition process of the information processing device related to this disclosure. This is a diagram illustrating driving data acquired by the driving data acquisition process of the information processing device related to this disclosure. This is a diagram for explaining the process of the deviation degree determination unit related to this disclosure. This is a flowchart showing an example of a process executed by the information processing device related to this disclosure. This is a diagram illustrating the functional configuration of the information processing device related to this disclosure. This is a diagram illustrating the functional configuration of the information processing device related to this disclosure. This is a diagram illustrating the process of a second example of the specific attribute operation identification unit related to this disclosure. This is a diagram illustrating the process of a second example of the specific attribute operation identification unit related to this disclosure. This is a flowchart showing an example of a process executed by the information processing device related to this disclosure. This is a diagram showing an application example of the information presentation system related to this disclosure.

[0011] The embodiments will be described below with reference to the drawings. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In addition, the same elements are denoted by the same reference numerals in each drawing, and redundant explanations have been omitted where necessary. Furthermore, although the following explanation will use drawings, the drawings used in the description of a particular embodiment are not limited to that embodiment. Each drawing may be applicable to all embodiments.

[0012] (Embodiment 1) Figure 1 is a diagram showing the configuration of an information processing device 1 according to the present disclosure. The information processing device 1 includes an image acquisition unit 2, an attribute identification unit 4, a driving data acquisition unit 6, a deviation degree determination unit 8, a behavior identification unit 10, and a presentation unit 12. The image acquisition unit 2 functions as an image acquisition means. The attribute identification unit 4 functions as an attribute identification means. The driving data acquisition unit 6 functions as a driving data acquisition means. The deviation degree determination unit 8 functions as a deviation degree determination means. The behavior identification unit 10 functions as a behavior identification means. The presentation unit 12 functions as a presentation means. The information processing device 1 uses these components to present characteristic behaviors of a vehicle driven by a driver with specific attributes.

[0013] The information processing device 1 can be implemented by a computer. The information processing device 1 can be implemented, for example, by cloud computing. Alternatively, the information processing device 1 can be implemented, for example, by multiple computers connected to each other in a communicative manner. Furthermore, each component of the information processing device 1 may be implemented by being distributed across multiple computers. In other words, the computers implementing each of the above-mentioned components may be physically separate. Also, the functions of each of the multiple components may be implemented by multiple computers.

[0014] Figure 2 is a flowchart illustrating an information processing method performed by the information processing device 1 according to this disclosure. It can also be said that Figure 2 shows an information presentation method performed by the information processing device 1. The image acquisition unit 2 acquires images obtained by photographing one or more points on which a vehicle can travel (step S12). Here, "points on which a vehicle can travel" are predetermined locations on a road that can be photographed by the imaging device. These locations are, for example, intersections, but are not limited to these. These locations may be parking areas or any points on a road.

[0015] Furthermore, an "image" is obtained by at least one imaging device capturing a location. The image may be a moving image (video) or a still image. The image may also be a frame image constituting a video. In the following, the term "image" also refers to "image data representing an image" as the object of processing in information processing.

[0016] The attribute identification unit 4 identifies the attributes of the driver of the vehicle shown in the image (step S14). Here, "driver's attributes" are defined, for example, by at least one of the driver's age group and gender, but are not limited to these. Also, "attributes" may be those that can be identified using the image acquired in the processing of S12. Specific examples of attribute identification methods will be described later.

[0017] The driving data acquisition unit 6 acquires driving data (step S16). Specifically, for each of at least two driver attributes, the driving data acquisition unit 6 acquires driving data indicating the vehicle's behavior at a given location, categorized by type of behavior. Here, "vehicle behavior" corresponds to the operations performed when the vehicle is driving. The types of behavior may include, but are not limited to, at least one of the following behaviors: stopping behavior, turning behavior, driving straight behavior, lane change behavior, and starting behavior.

[0018] Furthermore, the "driving data" may show the frequency distribution of each vehicle behavior. Specifically, the driving data may show the frequency distribution of values ​​for driving indicators that represent each vehicle behavior of drivers with each attribute at each location. The driving indicators correspond to indicators for evaluating the driving content for each behavior. The driving indicators correspond to the vehicle's actions performed by the driving operations when the vehicle performs each behavior. The driving indicators may include, for example, the driving speed. Also, for example, if the type of behavior is "stopping," the driving indicators may include the stopping position. The stopping position may include, for example, the distance from the stopping position of the vehicle to the stop line. Details of the driving data will be described later. The frequency distribution may be shown, for example, as a histogram.

[0019] The deviation degree determination unit 8 determines the degree of deviation between the overall driving data and the driving data relating to the driver of the first attribute (step S18). Specifically, for each type of behavior, the deviation degree determination unit 8 determines whether the degree of deviation between the overall driving data and the driving data relating to the vehicle of one or more drivers of a predetermined first attribute is large, using predetermined criteria. Here, "overall driving data" refers to the driving data relating to the vehicle of drivers of at least two attributes. Also, "first attribute" refers to an attribute predetermined to be used as a comparison target with the attribute relating to the overall driving data. In other words, "first attribute" corresponds to the attribute that is the subject of evaluation of the driving content. The first attribute may be, for example, "elderly person," but is not limited to this. Also, "degree of deviation" corresponds to the difference between "overall driving data" and "driving data relating to the first attribute." The larger the "degree of deviation," the greater the discrepancy between "driving data relating to the first attribute" and "overall driving data." Furthermore, the "judgment criteria" correspond to an index that can evaluate the degree of discrepancy between the "overall driving data" and the "driving data related to the first attribute."

[0020] Furthermore, the driving data may show the frequency distribution of each behavior of the vehicle. In this case, the deviation degree determination unit 8 determines whether the degree of deviation is large for each behavior between the frequency distribution of that behavior shown in the overall driving data and the frequency distribution of that behavior shown in the driving data for the first attribute. In this case, the determination criterion may be, for example, the difference between the modes of the two distributions, or the difference between the mean values ​​of the two distributions. In this case, the deviation degree determination unit 8 may determine that the degree of deviation is large if the difference between the modes is greater than or equal to a predetermined threshold. Alternatively, the deviation degree determination unit 8 may determine that the degree of deviation is large if the difference between the mean values ​​is greater than or equal to a predetermined threshold.

[0021] The behavior identification unit 10 identifies a first behavior relating to a driver with the first attribute (step S20). Specifically, the behavior identification unit 10 identifies the type of behavior that is determined to have a large degree of deviation as a first behavior, which is a characteristic behavior in the driving of a driver with the first attribute.

[0022] The presentation unit 12 performs processing to present the identified first behavior (step S22). For example, the presentation unit 12 may transmit information indicating the first behavior to a terminal of a destination facility or organization so that the first behavior is displayed on that terminal. The destination may be, for example, a facility or organization that provides driving instruction to drivers with the first attribute.

[0023] As described above, the information processing device 1 in this disclosure presents the characteristic behavior of a vehicle driven by a driver with a specific attribute, namely a first attribute. Because the information processing device 1 in this disclosure is configured in this way, the user receiving the information can appropriately grasp the characteristic behavior of a vehicle driven by a driver with a specific attribute. Therefore, it becomes possible to improve the driving skills of a driver with a specific attribute, such as by providing appropriate driving instruction to that driver.

[0024] Furthermore, the information processing method executed by the information processing device 1 also makes it possible to appropriately grasp the characteristic behavior of a vehicle driven by a driver with specific attributes. In addition, the program that executes the information processing method also makes it possible to appropriately grasp the characteristic behavior of a vehicle driven by a driver with specific attributes.

[0025] (Embodiment 2) Next, Embodiment 2 will be described with reference to the drawings. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In addition, the same elements are denoted by the same reference numerals in each drawing, and redundant explanations have been omitted as necessary. The contents of Embodiment 2 correspond to the details of the contents of Embodiment 1.

[0026] Figure 3 is a diagram illustrating the configuration of the information presentation system 20 according to this disclosure. The information presentation system 20 includes one or more roadside imaging devices 30, one or more terminal devices 40, a facility-side imaging device 50, and an information processing device 100. The roadside imaging device 30, the terminal devices 40, the facility-side imaging device 50, and the information processing device 100 have functions such as computers.

[0027] The information processing device 100 corresponds to the information processing device 1 shown in Figure 1. The information processing device 100 may be, for example, a server. Alternatively, the information processing device 100 may be, for example, an information terminal. Furthermore, the information processing device 100 may be implemented by, for example, cloud computing. Also, the information processing device 100 may be implemented by, for example, multiple computers that are connected to each other in a communicative manner. Furthermore, each component of the information processing device 100, which will be described later, may be implemented by multiple computers. As will be described later, the information processing device 100 identifies a specific attribute behavior (first behavior), which is a characteristic behavior of a vehicle driven by a driver with a specific attribute (first attribute), and presents this specific attribute behavior.

[0028] The roadside imaging device 30 and the information processing device 100 are connected via a wired or wireless network for communication. The information processing device 100 and the terminal device 40 are also connected via a wired or wireless network for communication. Furthermore, the terminal device 40 and the facility-side imaging device 50 are also connected via a wired or wireless network for communication. The wireless network may be, for example, a network using a communication line standard such as LTE (Long Term Evolution), or a network used in a specific area such as Wi-Fi (registered trademark) or local 5G. The wired network may be, for example, a LAN (Local Area Network) or optical fiber.

[0029] The roadside imaging device 30 is, for example, a camera, but is not limited to that. The roadside imaging device 30 is installed so as to be able to photograph the aforementioned locations, that is, locations where vehicles can travel. For example, the roadside imaging device 30 may be installed at an intersection. In this case, the roadside imaging device 30 is positioned so that the intersection is included in the shooting range. Note that roadside imaging devices 30 may be installed at each of multiple locations. Also, multiple roadside imaging devices 30 may be installed at a single location. Furthermore, if the roadside imaging device 30 is mounted on an aircraft such as a drone or artificial satellite, one roadside imaging device 30 may photograph multiple locations. The roadside imaging device 30 transmits the road image, which is an image obtained by photographing the installed location, to the information processing device 100.

[0030] Terminal device 40 is a terminal of the organization to which the information processing device 100 presents specific attribute behaviors. Terminal device 40 may be installed in a destination facility, which is a facility of the destination organization. Terminal device 40 receives information indicating specific attribute behaviors from the information processing device 100. Terminal device 40 outputs the received specific attribute behaviors to the user. The destination facility may be, for example, a driving school, but is not limited to this. The destination organization may also be a police organization. At the destination facility, driving instruction for drivers with specific attributes may be provided.

[0031] The facility-side imaging device 50 is installed at the destination facility. If the destination facility is a driving school, the facility-side imaging device 50 may capture scenes of a driver with specific attributes driving a vehicle at the destination facility. The images captured by the facility-side imaging device 50 are transmitted to the terminal device 40. The terminal device 40 may display the images. For example, a driver with specific attributes who has driven a vehicle at the destination facility can use the terminal device 40 to view scenes of their own driving.

[0032] Figure 4 is a diagram illustrating the hardware configuration of the information processing device 100 according to this disclosure. As shown in Figure 4, the information processing device 100 has as its main hardware components a control unit 102, a storage unit 104, a communication unit 106, and an interface unit 108 (IF). The control unit 102, storage unit 104, communication unit 106, and interface unit 108 are interconnected via a data bus or the like. The roadside imaging device 30, terminal device 40, and facility-side imaging device 50 described above may also have the hardware configuration of the information processing device 100 shown in Figure 4.

[0033] The control unit 102 is a processor, such as a CPU (Central Processing Unit). The control unit 102 has the function of an arithmetic unit that performs control processing and arithmetic processing. The control unit 102 may have multiple processors. The storage unit 104 is a storage device, such as a memory or a hard disk. The storage unit 104 is, for example, a ROM (Read Only Memory) or RAM (Random Access Memory). The storage unit 104 has the function of storing control programs and arithmetic programs executed by the control unit 102. In other words, the storage unit 104, which is a memory, stores one or more instructions. The storage unit 104 also has the function of temporarily storing processing data. The storage unit 104 may include a database. The storage unit 104 may also have multiple memories.

[0034] The communication unit 106 performs the processing necessary for the information processing device 100 to communicate with other devices via a network. The communication unit 106 may include a communication port, router, firewall, etc. The interface unit 108 (IF; Interface) is, for example, a user interface (UI). The interface unit 108 has an input device such as a keyboard, touch panel, or mouse, and an output device such as a display or speaker. The interface unit 108 may be configured such that the input device and the output device are integrated, for example, a touchscreen or touch panel. The interface unit 108 accepts data input operations from a user such as an operator or worker and outputs information to the user. The interface unit 108 may output information related to specific attribute behavior.

[0035] Figure 5 is a diagram illustrating the functional configuration of the information processing device 100 according to this disclosure. The information processing device 100 has as its components an image acquisition unit 120, an attribute identification unit 122, a trained model storage unit 124, a driving data acquisition unit 130, and a driving data storage unit 132. The information processing device 100 also has as its components a deviation degree determination unit 140, a behavior identification unit 160, and a behavior presentation unit 170. The behavior identification unit 160 also has a specific attribute behavior identification unit 162. The information processing device 100 identifies specific attribute behavior using these components and presents this specific attribute behavior to the destination facility.

[0036] As mentioned above, the information processing device 100 does not need to be composed of a single physical device. In this case, each of the above-mentioned components may be realized by multiple physically separate devices. This is also true in other embodiments described later.

[0037] The image acquisition unit 120 functions as an image acquisition means. The attribute identification unit 122 functions as an attribute identification means. The trained model storage unit 124 functions as a trained model storage means. The driving data acquisition unit 130 functions as a driving data acquisition means. The driving data storage unit 132 functions as a driving data storage means. The deviation degree determination unit 140 functions as a deviation degree determination means. The behavior identification unit 160 functions as a behavior identification means. The behavior presentation unit 170 functions as a behavior presentation means. The specific attribute behavior identification unit 162 functions as a specific attribute behavior identification means.

[0038] Each of the above-described components can be realized, for example, by executing a program under the control of the control unit 102. More specifically, each component can be realized by the control unit 102 executing a program (instruction) stored in the memory unit 104. Alternatively, each component can be realized by recording the necessary program on any non-volatile recording medium and installing it as needed. Furthermore, each component is not limited to being realized by software programs, but may also be realized by any combination of hardware, firmware, and software. In addition, each component may be realized using a user-programmable integrated circuit, such as an FPGA (field-programmable gate array) or a microcontroller. In this case, the program composed of the above-described components may be realized using this integrated circuit. These points are also true in other embodiments described later.

[0039] The image acquisition unit 120 corresponds to the image acquisition unit 2 shown in Figure 1. The image acquisition unit 120 acquires road images obtained by photographing one or more points where a vehicle can travel. Specifically, the image acquisition unit 120 acquires road images obtained by the roadside imaging device 30 from the roadside imaging device 30. The image acquisition unit 120 may also acquire road images by having the communication unit 106 receive road images from the roadside imaging device 30.

[0040] The image acquisition unit 120 can acquire road images from each of the multiple roadside imaging devices 30 installed at multiple locations. In this case, the road image is accompanied by location information indicating which location was captured. For example, the road image may be accompanied by identification information of the roadside imaging device 30 that captured the road image as location information. Alternatively, the road image may be accompanied by identification information or location information of the location where the roadside imaging device 30 that captured the road image is installed as location information.

[0041] The attribute identification unit 122 corresponds to the attribute identification unit 4 shown in Figure 1. The attribute identification unit 122 identifies the attributes of the drivers of vehicles shown in the road image. Specifically, if a vehicle shown in the road image has a mark indicating the driver's attributes, the attribute identification unit 122 may identify the driver's attributes as those indicated by the mark. For example, if a vehicle shown in the road image has an elderly driver mark, the attribute identification unit 122 identifies the driver's attribute as "elderly." Also, for example, if a vehicle shown in the road image has a beginner driver mark, the attribute identification unit 122 identifies the driver's attribute as "beginner." Also, for example, if a vehicle shown in the road image does not have either an elderly driver mark or a beginner driver mark, the attribute identification unit 122 identifies the driver's attribute as "a driver other than an elderly or beginner."

[0042] Furthermore, the attribute identification unit 122 may identify the attributes of the driver based on the face of the driver of the vehicle shown in the road image. For example, the attribute identification unit 122 may identify the age group of the driver based on the face of the driver of the vehicle shown in the road image. Also, the attribute identification unit 122 may identify the gender of the driver based on the face of the driver of the vehicle shown in the road image.

[0043] Further, the attribute specifying unit 122 may specify the attributes of the driver of the vehicle by using a learned model obtained by learning to output the attributes of the driver of the vehicle with the image in which the vehicle is reflected as an input. The learned model is generated by a machine learning algorithm such as a neural network. For example, the learned model may be generated by performing learning processing so as to output the attributes of the driver of the vehicle as "elderly person" with an image of a vehicle marked with an elderly person mark as an input. Also, for example, the learned model may be generated by performing learning processing so as to input an image of a vehicle driven by a driver of a certain age group A and output the attributes of the driver of the vehicle as "age group A". In this case, the learned model may estimate the age group of the driver from the face image of the driver.

[0044] Further, the attribute specifying unit 122 may specify a specific attribute X that is a certain specific attribute and an attribute Y that is the attribute of the driver excluding the specific attribute X. In this case, for example, if the specific attribute X is "elderly person", the attribute Y is "age groups other than the elderly". Also, the attribute specifying unit 122 may specify a specific attribute X that is a certain specific attribute, a specific attribute Y that is not the specific attribute X, and an attribute Z that is neither the specific attribute X nor the specific attribute Y. In this case, for example, the specific attribute X may be "elderly person (for example, 70 years old or older)", the specific attribute Y may be "young person (for example, 18 years old or older and less than 30 years old)", and the attribute Z may be "middle-aged person (for example, 30 years old or older and less than 70 years old)".

[0045] The learned model storage unit 124 stores the learned model used for specifying the attributes by the attribute specifying unit 122. Also, the learned model storage unit 124 may perform learning processing on the learned model as described above.

[0046] The driving data acquisition unit 130 corresponds to the driving data acquisition unit 6 shown in FIG. 1. The driving data acquisition unit 130 acquires driving data indicating the behavior of the vehicle at a location for each type of behavior for each of the attributes of at least two drivers. The driving data acquisition unit 130 may generate driving data indicating the behavior of the vehicle at each location by using the road image acquired by the image acquisition unit 120.

[0047] For example, the driving data acquisition unit 130 acquires driving data indicating the behavior of a vehicle driven by a driver with a specific attribute X at point A for each type of behavior. For example, the driving data acquisition unit 130 acquires driving data indicating the "behavior related to stopping" of a vehicle driven by a driver with a specific attribute X at point A. Similarly, the driving data acquisition unit 130 acquires driving data indicating each of the "behavior related to turning", "behavior related to going straight", "behavior related to lane change", and "behavior related to starting" of a vehicle driven by a driver with a specific attribute X at point A. Similarly, the driving data acquisition unit 130 acquires driving data indicating the behavior of a vehicle driven by a driver with an attribute other than the specific attribute X (for example, attribute Y) at point A for each type of behavior. Also, the driving data acquisition unit 130 acquires driving data in the same manner for a plurality of other points.

[0048] The driving data storage unit 132 stores the driving data acquired by the driving data acquisition unit 130. The driving data storage unit 132 stores the driving data for each attribute specified by the attribute specifying unit 122. Also, the driving data storage unit 132 stores the driving data for each type of behavior. Also, the driving data storage unit 132 may store the overall driving data for each type of behavior.

[0049] FIG. 6 is a diagram for explaining the driving data acquisition process by the information processing apparatus 100 according to the present disclosure. FIG. 6 shows an example in which two roadside imaging devices 30A and 30B are installed at a point Pt1 which is an intersection. Hereinafter, the process for point Pt1 will be described, but the same applies to other points. In FIG. 6, the broken line indicates the boundary of the imaging range of the roadside imaging device 30.

[0050] The image acquisition unit 120 acquires road images from each of the roadside imaging devices 30A and 30B. It is assumed that the road images are moving images. The attribute specifying unit 122 specifies the attributes of the drivers of the vehicles VcA, VcB, VcC, and VcD reflected in the road images as described above using the road images acquired from each of the roadside imaging devices 30A and 30B.

[0051] The driving data acquisition unit 130 synthesizes road images acquired from roadside imaging devices 30A and 30B to generate a road image showing the entire area of ​​point Pt1. For each of the vehicles VcA, VcB, VcC, and VcD shown in the road image, the driving data acquisition unit 130 acquires behavior data indicating driving indicators for each behavior at point Pt1, in association with the attributes of the driver of each vehicle. Examples of behavior data will be described later. The processing for vehicle VcA will be described below, but the same applies to vehicles VcB, VcC, and VcD.

[0052] The driving data acquisition unit 130 tracks vehicle VcA by detecting its position in each frame of the road image and acquires the trajectory of vehicle VcA at point Pt1. Furthermore, by detecting the position of vehicle VcA in each frame of the road image, it acquires the speed of vehicle VcA at point Pt1 from the distance traveled by vehicle VcA between frames and the frame rate.

[0053] Furthermore, the driving data acquisition unit 130 acquires behavioral data indicating driving indicators for each behavior from the trajectory and speed of the vehicle VcA at point Pt1. The driving data acquisition unit 130 may also acquire behavioral data indicating driving indicators for stopping behavior from the trajectory and speed of the vehicle VcA at point Pt1. For example, the driving data acquisition unit 130 may acquire the stopping position of the vehicle VcA as a driving indicator for the stopping behavior of the vehicle VcA. For example, the driving data acquisition unit 130 may acquire the relative position of the stopping position of the vehicle VcA with respect to the stop line as the stopping position of the vehicle VcA. Alternatively, for example, the driving data acquisition unit 130 may acquire the distance from the stop line to the stopping position of the vehicle VcA as the stopping position of the vehicle VcA. Alternatively, for example, the driving data acquisition unit 130 may acquire the deceleration of the vehicle VcA as a driving indicator for the stopping behavior of the vehicle VcA. For example, the driving data acquisition unit 130 may acquire the average speed from a position a predetermined distance before the stopping position of vehicle VcA to the stopping position as the degree of deceleration of vehicle VcA. Alternatively, for example, the driving data acquisition unit 130 may acquire the deceleration (negative acceleration) until vehicle VcA comes to a stop as the degree of deceleration of vehicle VcA.

[0054] Furthermore, the driving data acquisition unit 130 may acquire behavioral data indicating driving indicators of turning behavior from the trajectory and speed of vehicle VcA at point Pt1. For example, the driving data acquisition unit 130 may acquire a value indicating the degree of curvature of the curve of the trajectory when vehicle VcA makes a right or left turn, as a driving indicator of turning behavior for vehicle VcA. For example, the driving data acquisition unit 130 may acquire the radius of curvature of the curve as a value indicating the degree of curvature of the curve. Alternatively, for example, the driving data acquisition unit 130 may acquire the shortest distance between a predetermined position at the intersection (e.g., the center of the intersection) and the curve as a value indicating the degree of curvature of the curve. Alternatively, for example, the driving data acquisition unit 130 may acquire the speed when vehicle VcA is making a right or left turn, as a driving indicator of turning behavior for vehicle VcA.

[0055] Furthermore, the driving data acquisition unit 130 may acquire behavioral data indicating driving indicators for straight-line driving behavior from the trajectory and speed of vehicle VcA at point Pt1. For example, the driving data acquisition unit 130 may acquire the relative position of vehicle VcA's driving position in the lane as a driving indicator for straight-line driving behavior of vehicle VcA. In this case, for example, the driving data acquisition unit 130 may acquire the distance between the lane boundary line and the driving position of vehicle VcA. Alternatively, for example, the driving data acquisition unit 130 may acquire the speed of vehicle VcA when it is driving in a straight line as a driving indicator for straight-line driving behavior of vehicle VcA.

[0056] Furthermore, the driving data acquisition unit 130 may acquire behavioral data indicating driving indicators of lane change behavior from the trajectory and speed of vehicle VcA at point Pt1. For example, the driving data acquisition unit 130 may acquire a value indicating the timing of a lane change as a driving indicator of the lane change behavior of vehicle VcA. In this case, for example, when vehicle VcA changes lanes to a right-turn lane in order to make a right turn, the driving data acquisition unit 130 may acquire the distance between the point where vehicle VcA made the lane change and the stop line as a value indicating the timing of the lane change. Alternatively, for example, the driving data acquisition unit 130 may acquire the distance from the position where vehicle VcA started the lane change to the position where vehicle VcA finished the lane change as a value indicating the timing of the lane change. Alternatively, for example, the driving data acquisition unit 130 may acquire the time taken from when vehicle VcA started the lane change until it finished the lane change as a value indicating the timing of the lane change. Furthermore, for example, the driving data acquisition unit 130 may acquire the speed of vehicle VcA when it is changing lanes, as a driving index of the vehicle VcA's behavior regarding lane changes.

[0057] Furthermore, the driving data acquisition unit 130 may acquire behavioral data indicating driving indicators of starting behavior from the trajectory and speed of the vehicle VcA at point Pt1. For example, the driving data acquisition unit 130 may acquire a value indicating the timing of starting as a driving indicator of the starting behavior of the vehicle VcA. In this case, for example, the driving data acquisition unit 130 may acquire the time from when the traffic signal changes to an indication of "permission to proceed" (for example, the traffic signal turning "green") until the vehicle VcA starts moving as a value indicating the timing of starting. Alternatively, for example, the driving data acquisition unit 130 may acquire the acceleration when the vehicle VcA starts moving as a driving indicator of the starting behavior of the vehicle VcA.

[0058] Figure 7 is a diagram illustrating behavioral data acquired by the driving data acquisition unit 130 according to this disclosure. In the example of Figure 7, behavioral data for vehicles VcA, VcB, VcC, and VcD at point Pt1 is illustrated. In the example of Figure 7, the attribute of the driver of vehicle VcA is specified as specific attribute X. The attribute of the driver of vehicle VcB is specified as attribute Y. The attribute of the driver of vehicle VcC is specified as specific attribute X. The attribute of the driver of vehicle VcD is specified as attribute Y.

[0059] Furthermore, Figure 7 shows the values ​​of driving index A1 and driving index A2 for each of the vehicles VcA, VcB, VcC, and VcD in behavior A, as behavior data for behavior A. Also, Figure 7 shows the values ​​of driving index B1 and driving index B2 for each of the vehicles VcA, VcB, VcC, and VcD in behavior B, as behavior data for behavior B. Behavior A is, for example, "behavior related to stopping". Driving index A1 is, for example, "stopping position", and driving index A2 is, for example, "deceleration rate". Behavior B is, for example, "behavior related to turning". Driving index B1 is, for example, "degree of turning", and driving index B2 is, for example, "speed".

[0060] Furthermore, the driving data acquisition unit 130 aggregates the behavioral data for each attribute at point Pt1 to acquire driving data that shows the frequency distribution of each behavior for each attribute. In other words, the driving data can be said to be statistical data obtained by aggregating the behavioral data for each attribute. For example, the driving data acquisition unit 130 uses multiple behavioral data X1 of the driving index X1 of the behavior X of a vehicle driven by a driver with a specific attribute X to perform the following processing. The specific attribute X is, for example, "elderly person". Also, the behavior X is, for example, "behavior related to stopping", and the driving index X1 is, for example, "stopping position".

[0061] The driving data acquisition unit 130 calculates (counts) the frequency of values ​​that fit each interval (class) of the driving index X1 for a specific attribute X. This allows the driving data acquisition unit 130 to acquire driving data X1 that shows the frequency distribution of the driving index X1 for behavior X for the specific attribute X. Furthermore, the driving data acquisition unit 130 performs a similar process for other driving indexes of behavior X for the specific attribute X, thereby acquiring driving data for each driving index of behavior X for the specific attribute X. Similarly, the driving data acquisition unit 130 performs a similar process for other behaviors related to the specific attribute X, thereby acquiring driving data for each behavior related to the specific attribute X. Finally, the driving data acquisition unit 130 performs a similar process for other attributes Y, thereby acquiring driving data for each behavior related to attribute Y.

[0062] Furthermore, the driving data acquisition unit 130 acquires overall driving data, which is driving data relating to vehicles of drivers with at least two attributes for each type of behavior. For example, the driving data acquisition unit 130 acquires overall driving data X1 relating to vehicles of drivers with specific attribute X and attribute Y for the driving index X1 of behavior X. Specifically, the driving data acquisition unit 130 may acquire overall driving data X1 by performing the above-described processing using multiple behavior data X1 of the driving index X1 of behavior X by vehicles of drivers with specific attribute X and attribute Y. Alternatively, the driving data acquisition unit 130 may acquire overall driving data X1 by combining the driving data X1 for specific attribute X and the driving data X1 for attribute Y. In addition, the driving data acquisition unit 130 may acquire overall driving data relating to vehicles of drivers with all attributes.

[0063] Figures 8 and 9 illustrate driving data acquired by the driving data acquisition process of the information processing device 100 according to this disclosure. Figures 8 and 9 may show, for example, overall driving data relating to specific attribute X and attribute Y, or they may show driving data relating to specific attribute X.

[0064] Figure 8 illustrates driving data D11 and D12 related to "stopping behavior" at a certain point Pt1. Driving data D11 is a graph showing the frequency distribution of stopping positions. In the graph of driving data D11, the horizontal axis shows the stopping position, and the vertical axis shows the frequency for each section of the stopping position. A stopping position with a value greater than the position of the stop line indicates that the vehicle stopped beyond the stop line. Driving data D12 is a graph showing the frequency distribution of deceleration. In the graph of driving data D12, the horizontal axis shows the deceleration, and the vertical axis shows the frequency for each section of the deceleration. Mark M11 indicates the value of the stopping position of vehicle VcA, and mark M12 indicates the value of the deceleration of vehicle VcA.

[0065] Figure 9 illustrates driving data D21 and D22 related to the "behavior related to turning" when turning right at a certain point Pt1. Driving data D21 is a graph showing the frequency distribution of turning degree. In the graph of driving data D21, the horizontal axis shows the turning degree, and the vertical axis shows the frequency for each section of the turning degree. A larger turning degree value indicates a larger radius of curvature. Driving data D22 is a graph showing the frequency distribution of speed during the right turn. In the graph of driving data D22, the horizontal axis shows the speed, and the vertical axis shows the frequency for each section of the speed. Mark M21 indicates the turning degree value of vehicle VcA when turning right. Mark M22 indicates the speed of vehicle VcA during the right turn.

[0066] The deviation degree determination unit 140 corresponds to the deviation degree determination unit 8 shown in Figure 1. The deviation degree determination unit 140 compares the overall driving data with the driving data relating to drivers with specific attributes. The deviation degree determination unit 140 then determines the degree of deviation between the overall driving data and the driving data relating to drivers with specific attributes. Specifically, for each type of behavior, the deviation degree determination unit 140 determines, using predetermined criteria, whether the degree of deviation between the overall driving data and the driving data relating to the vehicles of one or more drivers with specific attributes is large or small. For example, for each type of behavior, the deviation degree determination unit 140 determines whether the degree of deviation between the frequency distribution of the behavior shown in the overall driving data and the frequency distribution of the behavior shown in the driving data relating to the specific attributes is large or small.

[0067] The deviation degree determination unit 140 then determines that the deviation degree for a certain behavior is large if the deviation degree for that behavior meets the determination criteria. For example, the deviation degree determination unit 140 determines that the deviation degree for a certain behavior is large if the deviation degree between the overall driving data and the driving data for a vehicle driven by a driver with specific attributes is greater than or equal to a predetermined threshold.

[0068] Figure 10 is a diagram illustrating the processing of the deviation degree determination unit 140 according to this disclosure. Figure 10 illustrates driving data for the driving index "degree of turning" in "behavior related to turning" at a certain point. The dashed line shows the overall driving data D31. The overall driving data D31 is a graph showing the frequency distribution of the degree of turning for vehicles driven by drivers with at least two attributes. The solid line shows the driving data D32 for vehicles driven by drivers with specific attribute X. The driving data D32 is a graph showing the frequency distribution of the degree of turning for vehicles driven by drivers with specific attribute X. Note that although Figure 10 shows "behavior related to turning" as an example of a type of behavior, the same applies to the degree of deviation for other behaviors. The deviation degree determination unit 140 determines the degree of deviation between the frequency distribution of the degree of turning shown in the overall driving data D31 and the frequency distribution of the degree of turning shown in the driving data D32 related to specific attribute X.

[0069] In the example shown in Figure 10, for example, the deviation degree determination unit 140 may determine the deviation degree as the difference between the mode of the frequency distribution of the overall driving data D31 and the mode of the frequency distribution of the driving data D32 relating to a specific attribute X. The deviation degree determination unit 140 may then determine that the deviation degree for "behavior related to turning" is large if the difference between the mode of the frequency distribution of the overall driving data D31 and the mode of the frequency distribution of the driving data D32 is greater than or equal to a predetermined threshold. In particular, in this case, the deviation degree determination unit 140 may determine that the deviation degree for the degree of turning in "behavior related to turning" is large. The same applies when using the mean or median, as described later.

[0070] Alternatively, for example, the deviation degree determination unit 140 may determine the deviation degree by the difference between the average value of the frequency distribution of the overall driving data D31 and the average value of the frequency distribution of the driving data D32 relating to a specific attribute X. Alternatively, for example, the deviation degree determination unit 140 may determine the deviation degree by the difference between the median value of the frequency distribution of the overall driving data D31 and the median value of the frequency distribution of the driving data D32 relating to a specific attribute X.

[0071] Furthermore, for example, the deviation degree determination unit 140 may determine the degree of deviation by determining that the smaller the area of ​​overlap between the frequency distribution of the overall driving data D31 and the frequency distribution of the driving data D32 relating to a specific attribute X, the greater the degree of deviation. The deviation degree determination unit 140 may also determine that the degree of deviation regarding "behavior related to turning" is large if the area of ​​overlap between the frequency distribution of the overall driving data D31 and the frequency distribution of the driving data D32 is below a predetermined threshold. In particular, in this case, the deviation degree determination unit 140 may determine that the degree of deviation regarding the degree of turning in "behavior related to turning" is large.

[0072] Furthermore, for example, the deviation degree determination unit 140 may set a target value for "behavior related to turning" using the overall driving data D31. The deviation degree determination unit 140 may then use the difference between the set target value and the driving data D32 related to the specific attribute X as the deviation degree. Alternatively, for example, the deviation degree determination unit 140 may use the difference between the mean and variance in the overall driving data D31 and the mean and variance in the driving data D32 related to the specific attribute X as the deviation degree.

[0073] The behavior identification unit 160 corresponds to the behavior identification unit 10 shown in Figure 1. The behavior identification unit 160 identifies behavior related to a specific attribute. The specific attribute behavior identification unit 162 identifies the type of behavior that the deviation degree determination unit 140 has determined to have a large deviation degree as a specific attribute behavior (first behavior), which is a characteristic behavior in the driving of a driver with a specific attribute. In the example of Figure 10, the specific attribute behavior identification unit 162 identifies "behavior related to turning" as a specific attribute behavior characteristic of the driving of a driver with specific attribute X. In particular, in the example of Figure 10, the specific attribute behavior identification unit 162 may identify the degree of turning in "behavior related to turning" as a specific attribute behavior characteristic of the driving of a driver with specific attribute X.

[0074] Furthermore, specific attribute behaviors correspond to driving data related to the behavior of a specific attribute that shows a large degree of deviation from the overall driving data. Therefore, specific attribute behaviors may represent behaviors that drivers with that specific attribute are not good at compared to drivers with the attributes corresponding to the overall driving data. For example, if "behavior related to turning" is a specific attribute behavior, then drivers with that specific attribute may be bad at "behavior related to turning," that is, turning operations such as turning right or left or driving around curves.

[0075] Furthermore, the deviation degree determination unit 140 may determine the degree of deviation for each behavior using driving data obtained from multiple locations. That is, for each behavior, the deviation degree determination unit 140 may determine whether the degree of deviation between the overall driving data and the driving data related to the specific attribute is large at each of the multiple locations. Then, if the specific attribute behavior identification unit 162 determines that the degree of deviation is large for behavior X at a predetermined number of locations or more, it may identify behavior X as a specific attribute behavior X (first behavior), which is a characteristic behavior of the driving of a driver with specific attribute X.

[0076] The behavior presentation unit 170 corresponds to the presentation unit 12 shown in Figure 1. The behavior presentation unit 170 performs processing to present the specific attribute behavior identified by the behavior identification unit 160 to the destination facility. Specifically, the behavior presentation unit 170 performs processing to transmit information regarding the specific attribute behavior to the terminal device 40. The behavior presentation unit 170 may also control the communication unit 106 to transmit information regarding the specific attribute behavior to the terminal device 40. Furthermore, the behavior presentation unit 170 may perform processing to transmit an instruction to the terminal device 40 to display the information regarding the specific attribute behavior on the display of the terminal device 40.

[0077] The behavior presentation unit 170 then transmits information regarding specific attribute behavior to the terminal device 40, which then displays the information regarding the specific attribute behavior. In this way, the specific attribute behavior is presented to the receiving facility. By presenting the specific attribute behavior to the receiving facility in this manner, the manager of the receiving facility can easily grasp the specific attribute behavior. Therefore, the receiving facility can provide appropriate driving guidance to drivers with specific attributes.

[0078] For example, if the facility being presented is a driving school, the manager of the facility can plan the training so that specific attribute behaviors can be reproduced at the driving school. When a driver with specific attributes drives at the driving school and exhibits those specific attribute behaviors, the facility's imaging device 50 may capture that scene. The manager can then allow the driver with specific attributes to view the image of that scene, enabling effective instruction for the driver with specific attributes. Therefore, it becomes possible to improve the driving skills of drivers with specific attributes.

[0079] Figure 11 is a flowchart illustrating an example of processing performed by the information processing device 100 according to this disclosure. Figure 11 also shows an information processing method performed by the information processing device 100 according to this disclosure. Furthermore, Figure 11 can also be said to show an information presentation method performed by the information processing device 100 according to this disclosure.

[0080] Furthermore, if each component shown in Figure 5 is implemented by cloud computing or multiple computers, the process illustrated in Figure 11 will be executed by the information processing device 100 implemented by cloud computing or multiple computers. Please note that the process described in Figure 11 is merely an example. These points also apply to other flowcharts.

[0081] As described above, the image acquisition unit 120 acquires road images obtained by photographing one or more locations where a vehicle can travel (step S100). As described above, the attribute identification unit 122 identifies the attributes of the drivers of the vehicles shown in the road images (step S102). As described above, the driving data acquisition unit 130 acquires behavior data indicating driving indicators for each behavior of each vehicle at each location (step S104). As described above, the driving data acquisition unit 130 uses the behavior data to acquire driving data (specific attribute driving data) for each behavior related to the vehicle of a driver with specific attributes (step S106). As described above, the driving data acquisition unit 130 uses the behavior data to acquire overall driving data for each behavior related to the vehicle of a driver with at least two attributes (step S108).

[0082] As described above, the deviation degree determination unit 140 determines the degree of deviation between the overall driving data and the specific attribute driving data (step S110). As described above, the behavior identification unit 160 identifies the specific attribute behavior, which is the characteristic behavior of a driver with specific attribute X (step S112). As described above, the behavior presentation unit 170 performs processing to present the specific attribute behavior to the presentation destination facility (step S114).

[0083] In the example described above, the overall driving data was assumed to be driving data for vehicles driven by drivers with at least two attributes. On the other hand, in order to identify specific attribute behaviors, it is preferable to have a larger number of driver attributes in the overall driving data. Therefore, the overall driving data may be driving data for vehicles driven by drivers with all attributes.

[0084] (Embodiment 3) Next, Embodiment 3 will be described with reference to the drawings. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In addition, the same elements are denoted by the same reference numerals in each drawing, and redundant explanations have been omitted as necessary. The information presentation system 20 according to Embodiment 3 is substantially the same as that shown in Figure 3, so its description will be omitted. The hardware configuration of the information processing device according to Embodiment 3 is substantially the same as that shown in Figure 4, so its description will be omitted.

[0085] Figure 12 is a diagram illustrating the functional configuration of the information processing device 100 according to the present disclosure. Similar to Embodiment 2, the information processing device 100 has as its components an image acquisition unit 120, an attribute identification unit 122, a trained model storage unit 124, a driving data acquisition unit 130, and a driving data storage unit 132. The information processing device 100 also has as its components a deviation degree determination unit 140, a behavior identification unit 160, and a behavior presentation unit 170. The behavior identification unit 160 also has a specific attribute behavior identification unit 162. In Embodiment 3, the information processing device 100 also has an accident information acquisition unit 150. The behavior identification unit 160 also has an accident-related behavior identification unit 164. The accident information acquisition unit 150 functions as an accident information acquisition means. The accident-related behavior identification unit 164 functions as an accident-related behavior identification means.

[0086] The accident information acquisition unit 150 acquires accident information relating to accidents involving vehicles driven by drivers with specific attributes (first attributes). The accident information acquisition unit 150 may acquire accident information transmitted from, for example, a server of an organization that provides accident information. In this case, the accident information acquisition unit 150 can be implemented by the communication unit 106. Alternatively, the accident information acquisition unit 150 may acquire accident information entered by an administrator operating the interface unit 108.

[0087] Accident information is information concerning traffic accidents caused by the driving of a driver with specific characteristics. Accident information may be generated using information obtained from, for example, police organizations. Here, accident information indicates the type of accident for each of several accidents caused by a driver with specific characteristics. The type of accident can be classified, for example, by the location of the accident, the object involved in the accident, the circumstances at the time of the accident, and the cause of the accident. The location of the accident corresponds to, for example, an "intersection," "straight road," "curved road," or "pedestrian crossing." The object involved in the accident is an object that collides with or comes into contact with the vehicle of the driver with specific characteristics. The object involved in the accident corresponds to, for example, a "moving vehicle," a "stopped vehicle," a "pedestrian," or an "artificial object." The circumstances at the time of the accident correspond to, for example, "turning right," "turning left," "driving straight," "driving on a curved road," "changing lanes," "stopped," or "starting." The cause of the accident corresponds to, for example, "delayed braking," "driving against traffic," "inattentive driving," "failure to stop sign," "speeding," "running a red light," "overlooking another vehicle or pedestrian," "overrunning a station," etc. Furthermore, the causes of accidents can be attributed to factors such as "perceptual errors," "judgment errors," or "operational errors."

[0088] The accident-related behavior identification unit 164 identifies accident-related behavior (second behavior) based on accident information, specifically the behavior of a vehicle driven by a driver with specific attributes in relation to an accident. More specifically, the accident-related behavior identification unit 164 identifies behavior associated with accidents of a type that occur in a high number of occurrences among accidents of a vehicle driven by a driver with specific attributes. More specifically, the accident-related behavior identification unit 164 identifies behavior associated with accidents of a type that occur in a number of occurrences that ranks above a predetermined rank among accidents of a vehicle driven by a driver with specific attributes.

[0089] Here, each type of accident shown in the accident information may be related to the vehicle behavior described above. For example, if the type of accident corresponds to "stopping," this type of accident may be related to "behavior related to stopping." Also, if the type of accident corresponds to "turning right," "turning left," or "driving on a curved road," this type of accident may be related to "behavior related to turning." Also, if the type of accident corresponds to "driving straight," this type of accident may be related to "behavior related to driving straight." Also, if the type of accident corresponds to "changing lanes," this type of accident may be related to "behavior related to changing lanes." Also, if the type of accident corresponds to "starting," this type of accident may be related to "behavior related to starting."

[0090] For example, suppose the predetermined ranking is 3rd. And suppose the type of accident with the highest number of occurrences corresponds to "turning right", the type of accident with the second highest number of occurrences corresponds to "turning left", and the type of accident with the third highest number of occurrences corresponds to "changing lanes". In this case, the accident-related behavior identification unit 164 identifies "behavior related to turning" and "behavior related to changing lanes" as accident-related behaviors.

[0091] The accident-related behavior identification unit 164 may store in advance information (dictionary, table, etc.) that associates the type of accident with the behavior associated with it, as described above. The accident-related behavior identification unit 164 may then use this information to identify accident-related behavior. Alternatively, the accident-related behavior identification unit 164 may use keywords described in the accident information to identify behavior related to the accident. For example, the accident-related behavior identification unit 164 may have dictionary data in advance that shows one or more keywords related to each of the behaviors described above. If the accident information contains keywords related to behavior A, the accident-related behavior identification unit 164 may identify behavior A as being related to the accident corresponding to that accident information and identify behavior A as an accident-related behavior. Or, if the similarity between the keywords included in the accident information and behavior A is above a predetermined threshold, the accident-related behavior identification unit 164 may identify behavior A as being related to the accident corresponding to that accident information and identify behavior A as an accident-related behavior. Alternatively, the accident-related behavior identification unit 164 may identify accident-related behaviors using a trained model generated by learning a machine learning algorithm, which takes accident information as input and outputs accident-related behaviors corresponding to that accident information.

[0092] Furthermore, the accident-related behavior identification unit 164 identifies behaviors that are both specific attribute behaviors (first behaviors) and accident-related behaviors (second behaviors) as behaviors requiring attention (third behaviors). For example, if "behavior related to turning" is both a specific attribute behavior and an accident-related behavior, the accident-related behavior identification unit 164 identifies "behavior related to turning" as a behavior requiring attention.

[0093] The behavior presentation unit 170 performs processing to present the accident-related behavior identified by the accident-related behavior identification unit 164 to the destination facility. The behavior presentation unit 170 also performs processing to present the behavior requiring attention identified by the accident-related behavior identification unit 164 to the destination facility. Specifically, the behavior presentation unit 170 performs processing to transmit information regarding the accident-related behavior and the behavior requiring attention to the terminal device 40. The behavior presentation unit 170 may also control the communication unit 106 to transmit information regarding the accident-related behavior and the behavior requiring attention to the terminal device 40. Furthermore, the behavior presentation unit 170 may perform processing to transmit an instruction to the terminal device 40 to display the information regarding the accident-related behavior and the behavior requiring attention on the display of the terminal device 40.

[0094] The behavior presentation unit 170 then transmits information regarding accident-related behaviors and behaviors requiring attention to the terminal device 40, which then displays the information regarding accident-related behaviors and behaviors requiring attention. In this way, accident-related behaviors and behaviors requiring attention are presented to the receiving facility. By presenting accident-related behaviors and behaviors requiring attention to the receiving facility in this manner, the manager of the receiving facility can easily grasp these behaviors. Therefore, the receiving facility can provide appropriate driving guidance to drivers with specific characteristics. In other words, the receiving facility can provide particularly focused guidance to drivers with specific characteristics regarding behaviors that are characteristic of drivers with specific characteristics and that may induce accidents.

[0095] For example, if the facility to which the information is presented is a driving school, the manager of the facility can plan the training so that the dangerous driving behavior can be reproduced at the driving school. When a driver with specific characteristics drives at the driving school and exhibits dangerous driving behavior, the facility's imaging device 50 may capture the scene. The manager can then allow the driver with specific characteristics to view the image of that scene, enabling effective guidance for that driver. Therefore, it becomes possible to reduce the occurrence of accidents caused by drivers with specific characteristics.

[0096] Figure 13 is a flowchart illustrating an example of processing performed by the information processing device 100 according to this disclosure. Figure 13 also illustrates an information processing method performed by the information processing device 100 according to this disclosure. Furthermore, Figure 13 can be said to illustrate an information presentation method performed by the information processing device 100 according to this disclosure.

[0097] The information processing device 100 performs substantially the same processing as described in S100 to S112 using Figure 11 (step S202). The accident information acquisition unit 150 acquires accident information relating to specific attributes as described above (step S220). The behavior identification unit 160 identifies accident-related behaviors associated with accidents caused by drivers with specific attributes as described above (step S222). The behavior identification unit 160 also identifies behaviors that are specific attribute behaviors and accident-related behaviors that require attention as described above (step S224). The behavior presentation unit 170 performs processing to present the specific attribute behaviors, accident-related behaviors, and behaviors that require attention to the destination facility as described above (step S226).

[0098] (Embodiment 4) Next, Embodiment 4 will be described with reference to the drawings. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In addition, the same elements are denoted by the same reference numerals in each drawing, and redundant explanations have been omitted as necessary. The information presentation system 20 according to Embodiment 4 is substantially the same as that shown in Figure 3, so its description will be omitted. The hardware configuration of the information processing device according to Embodiment 4 is substantially the same as that shown in Figure 4, so its description will be omitted.

[0099] Figure 14 is a diagram illustrating the functional configuration of the information processing device 100 according to the present disclosure. Similar to Embodiment 3, the information processing device 100 has as its components an image acquisition unit 120, an attribute identification unit 122, a trained model storage unit 124, a driving data acquisition unit 130, and a driving data storage unit 132. The information processing device 100 also has as its components a deviation degree determination unit 140, an accident information acquisition unit 150, a behavior identification unit 160, and a behavior presentation unit 170. The behavior identification unit 160 also has a specific attribute behavior identification unit 162 and an accident-related behavior identification unit 164. In Embodiment 4, the behavior identification unit 160 also has a specific attribute operation identification unit 166 and an accident-related operation identification unit 168. The specific attribute operation identification unit 166 functions as a specific attribute operation identification means. The accident-related operation identification unit 168 functions as an accident-related operation identification means.

[0100] The specific attribute operation identification unit 166 identifies a specific attribute operation (first operation) that is unique to a driver with a specific attribute (first attribute) in a specific attribute behavior (first behavior). Here, "operation in behavior X" corresponds to the vehicle's operation performed by the operation when the vehicle performs behavior X. Also, "operation in behavior X" corresponds to the specific way the vehicle moves when behavior X is performed, as expressed by the driving indicators of behavior X. And "specific attribute operation X" corresponds to the specific movement habits of a driver with a specific attribute in specific attribute behavior X. Specifically, "specific attribute operation X" corresponds to the specific movement habits of a driver with a specific attribute in specific attribute behavior X, compared with the vehicle movement of a driver with an attribute corresponding to the overall driving data. More specifically, "specific attribute operation X" corresponds to the trend of the driving indicators in the driving data relating to a driver with a specific attribute, compared with the trend of the driving indicators in the overall driving data for specific attribute behavior X.

[0101] For example, if behavior X is "behavior related to stopping," then "action in behavior X" corresponds to how much the vehicle decelerates when it stops, and at what position relative to the stop line the vehicle stops. And, if behavior X is "behavior related to stopping," then "specific attribute action X" corresponds, for example, that a vehicle driven by a driver with a specific attribute decelerates with a large rate of deceleration when it stops, and that a vehicle driven by a driver with a specific attribute stops beyond the stop line.

[0102] Furthermore, for example, if behavior X is "behavior related to turning," then "action in behavior X" corresponds to how much the vehicle turns and at what speed the vehicle turns. And if specific attribute behavior X is "behavior related to turning," then "specific attribute action X" corresponds, for example, a vehicle driven by a driver with specific attributes making a wide turn around a curve, and a vehicle driven by a driver with specific attributes turning at high speed.

[0103] Furthermore, for example, if behavior X is "behavior related to driving in a straight line," then "action in behavior X" corresponds to the position of the vehicle within the lane and the speed at which the vehicle is driving in a straight line. And if specific attribute behavior X is "behavior related to driving in a straight line," then "specific attribute action X" corresponds to, for example, a vehicle driven by a driver with specific attributes crossing the lane boundary and a vehicle driven by a driver with specific attributes driving in a straight line at high speed.

[0104] Furthermore, for example, if behavior X is "behavior related to lane changes," then "action in behavior X" corresponds to when the vehicle changes lanes and how fast the vehicle travels while changing lanes. And if specific attribute behavior X is "behavior related to lane changes," then "specific attribute action X" corresponds, for example, a vehicle driven by a driver with a specific attribute changing lanes at a late timing, and a vehicle driven by a driver with a specific attribute changing lanes at a high speed.

[0105] Furthermore, for example, if behavior X is "behavior related to starting," then "action in behavior X" corresponds to when the vehicle starts and how much acceleration the vehicle has when starting. And if specific attribute behavior X is "behavior related to starting," then "specific attribute action X" corresponds, for example, that the vehicle of a driver with a specific attribute starts at a late timing and that the vehicle of a driver with a specific attribute starts with high acceleration.

[0106] The specific attribute behavior identification unit 166 may, as part of the processing in the first example, identify an action as a specific attribute behavior X if the frequency of an action performed by a driver with a specific attribute is higher than the frequency of the same action performed by a driver with an attribute corresponding to the overall driving data. For example, in the example of Figure 10, suppose the deviation degree determination unit 140 determines that there is a large deviation between the frequency distribution of turning degrees shown in the overall driving data D31 and the frequency distribution of turning degrees shown in the driving data D32 related to specific attribute X. In this case, the specific attribute behavior identification unit 162 identifies the "behavior related to turning" as specific attribute behavior X. In the example of Figure 10, regarding the driving index "turning degree" in the "behavior related to turning," the frequency of sections with large turning degrees is higher in the driving data D32 related to specific attribute X compared to the overall driving data D31.

[0107] Therefore, in the "behavior related to turning," the specific attribute operation identification unit 166 identifies a large turning angle of a vehicle driven by a driver with specific attribute X as specific attribute operation X. In other words, in the "behavior related to turning," the specific attribute operation identification unit 166 identifies a wide turning angle of a vehicle driven by a driver with specific attribute X as specific attribute operation X.

[0108] As in the first example, by the specific attribute operation identification unit 166 identifying specific attribute operation X, it is possible to identify operations (habits) specific to the driver with specific attribute X, compared to the driver with attributes corresponding to the overall driving data, that is, the driver with attributes other than specific attribute X. For example, if specific attribute X is elderly, the specific attribute operation identification unit 166 can identify habits specific to elderly drivers, compared to the driving of drivers of other age groups.

[0109] Furthermore, as part of the processing in the second example, the specific attribute operation identification unit 166 may identify specific attribute operation X using driving data relating to specific attribute behavior X by a driver with specific attributes. Specifically, the specific attribute operation identification unit 166 may identify operation that deviates from a normal distribution obtained using driving data relating to specific attribute behavior X by a driver with specific attributes as specific attribute operation X. The following will be explained with reference to Figures 15 and 16.

[0110] Figures 15 and 16 are diagrams illustrating the processing of a second example of the specific attribute operation identification unit 166 according to this disclosure. Figures 15 and 16 illustrate driving data for the driving index "degree of turning" in "behavior related to turning" at a certain point. In Figure 15, the dashed line shows the overall driving data D41. The overall driving data D41 is a graph showing the frequency distribution of the degree of turning for vehicles driven by drivers with at least two attributes. Also, in Figures 15 and 16, the solid line shows the driving data D42 for vehicles driven by drivers with specific attribute X. The driving data D42 is a graph showing the frequency distribution of the degree of turning for vehicles driven by drivers with specific attribute X.

[0111] Here, in the overall driving data D41, there is a tendency for the frequency to decrease as the degree of curvature increases from the mode. Also, the frequency distribution of driving data D42 is skewed towards sections with larger degrees of curvature compared to the frequency distribution of overall driving data D41. Furthermore, the section of curvature indicated by mark M41 has a frequency of almost zero in the overall driving data D41. In other words, the section of curvature indicated by mark M41 deviates from the trend of operation in the overall driving data D41. Therefore, it is highly likely that drivers with the attributes corresponding to the overall driving data D41 rarely perform the type of turning operation indicated by mark M41. Moreover, the section of curvature indicated by mark M41 is a section with a considerably larger degree of curvature compared to the section with the mode in driving data D42. However, the frequency of the section of curvature corresponding to mark M41 is higher compared to the preceding section.

[0112] In Figure 16, the dashed line represents the normal distribution D43 obtained using the driving data D42. The specific attribute operation identification unit 166 may generate the normal distribution D43 using the driving data D42. The normal distribution D43 can be calculated using the mean and variance (standard deviation) of the driving data D42. Here, as shown in Figure 16, in the interval indicated by mark M41, the frequency in the driving data D42 is higher than the frequency in the normal distribution D43. In other words, in the interval indicated by mark M41, the difference obtained by subtracting the frequency in the normal distribution D43 from the frequency in the driving data D42 is greater than a predetermined threshold. In this case, it can be said that the operation corresponding to the interval indicated by mark M41 in the driving data D42 deviates from the normal distribution D43.

[0113] In this case, the specific attribute operation identification unit 166 identifies the operation corresponding to the section indicated by mark M41, that is, the operation with a large degree of turning, as specific attribute operation X. As mentioned above, the section of turning indicated by mark M41 is a section with a considerably large degree of turning compared to the section of the mode in the driving data D42. Therefore, an operation with a large degree of turning, such as the section indicated by mark M41, is an abnormal operation compared to the overall tendency of drivers with specific attribute X. However, the frequency of the section of turning indicated by mark M41 in the driving data D42 is higher than the frequency in the normal distribution D43. Therefore, it can be said that an operation with a large degree of turning, such as the section indicated by mark M41, is an abnormal operation that a non-negligible proportion of drivers with specific attribute X may perform. Therefore, it can be said that the operation corresponding to the section of turning indicated by mark M41 is an operation specific to drivers with specific attribute X. Therefore, by identifying specific attribute behavior X in the manner of processing in the second example, it is possible to identify abnormal behaviors that a non-negligible proportion of drivers with specific attribute X may perform as behaviors unique to drivers with specific attribute X.

[0114] The accident-related action identification unit 168 identifies actions related to the cause of an accident caused by a vehicle driven by a driver with specific attributes, based on the accident information, as accident-related actions (second actions). Specifically, the accident-related action identification unit 168 identifies actions related to accident causes that occur in large numbers among the causes of accidents caused by vehicles driven by drivers with specific attributes as accident-related actions. More specifically, the accident-related action identification unit 168 identifies actions related to accident causes that occur in large numbers among the causes of accidents caused by vehicles driven by drivers with specific attributes, where the ranking of the number of accidents caused by that cause is at or above a predetermined rank, as accident-related actions.

[0115] Here, each of the accident causes indicated in the accident information may be related to the vehicle's behavior as described above. For example, if the cause of the accident is "sudden braking while stopping," this cause may be related to "behavior related to stopping" specifically to "vehicles driven by drivers with specific characteristics slowing down with a large deceleration rate when stopping." Also, if the cause of the accident corresponds to "stopping beyond the stop line," this cause may be related to "behavior related to stopping" specifically to "vehicles driven by drivers with specific characteristics stopping beyond the stop line."

[0116] Furthermore, if the cause of the accident corresponds to "making a wide turn when turning right" or "overrunning a curved road," the cause of this accident may be related to "the action of a vehicle driven by a driver with specific characteristics making a wide turn around a curve" under "behavior related to turning." Furthermore, if the cause of the accident corresponds to "driving at high speed when turning right" or "driving at high speed while driving on a curved road," the cause of this accident may be related to "the action of a vehicle driven by a driver with specific characteristics making a turn at high speed" under "behavior related to turning."

[0117] Furthermore, if the cause of the accident corresponds to "deviation from the lane while driving straight," the cause of this accident may be related to "behavior related to driving straight" specifically to "vehicles driven by drivers with specific attributes crossing lane boundaries." Also, if the cause of the accident corresponds to "driving at high speed while driving straight," the cause of this accident may be related to "behavior related to driving straight" specifically to "vehicles driven by drivers with specific attributes traveling straight at high speed."

[0118] Furthermore, if the cause of the accident corresponds to "delay in the timing of the lane change," the cause of this accident may be related to "the action of a vehicle driven by a driver with specific attributes making a lane change at a late timing" under "behavior related to lane changes." Also, if the cause of the accident corresponds to "changing lanes at high speed," the cause of this accident may be related to "the action of a vehicle driven by a driver with specific attributes making a lane change at high speed" under "behavior related to lane changes."

[0119] Furthermore, if the cause of the accident corresponds to "failure to start even after the traffic signal changes," the cause of this accident may be related to "behavior related to starting," specifically "vehicles driven by drivers with specific attributes starting at a late timing." Also, if the cause of the accident corresponds to "sudden acceleration," the cause of this accident may be related to "behavior related to starting," specifically "vehicles driven by drivers with specific attribute X starting with high acceleration."

[0120] For example, suppose the predetermined ranking is 3rd. And suppose the cause of the accident with the highest number of occurrences corresponds to "making a wide turn while turning right", the cause of the accident with the second highest number of occurrences corresponds to "sudden braking while stopped", and the cause of the accident with the third highest number of occurrences corresponds to "delayed timing of lane change". In this case, the accident-related action identification unit 168 identifies "the action of a vehicle driven by a driver with specific attributes making a wide turn around a curve" as an accident-related action. Similarly, the accident-related action identification unit 168 identifies "the action of a vehicle driven by a driver with specific attributes decelerating with a large deceleration rate when stopped" and "the action of a vehicle driven by a driver with specific attributes making a lane change at a late timing" as accident-related actions.

[0121] The accident-related action identification unit 168 may pre-store information (dictionary, table, etc.) that associates the cause of an accident with the action related thereto, as described above. The accident-related action identification unit 168 may then use this information to identify an accident-related action. Alternatively, the accident-related action identification unit 168 may use keywords described in the accident information to identify an action related to the cause of an accident. For example, the accident-related action identification unit 168 may pre-store dictionary data indicating one or more keywords related to each of the actions described above. If the accident information contains keywords related to action A, the accident-related action identification unit 168 may identify action A as being related to the cause of the accident corresponding to that accident information, and identify action A as an accident-related action. Or, if the similarity between the keywords in the accident information and action A is above a predetermined threshold, the accident-related action identification unit 168 may identify action A as being related to the cause of the accident corresponding to that accident information, and identify action A as an accident-related action. Alternatively, the accident-related action identification unit 168 may identify accident-related actions by using a trained model generated by learning a machine learning algorithm, which takes accident information as input and outputs actions related to the cause of the accident corresponding to that accident information.

[0122] Furthermore, the accident-related action identification unit 168 identifies actions that are both specific attribute actions (first actions) and accident-related actions (second actions) as actions requiring attention (third actions). For example, if "the action of a vehicle driven by a driver with specific attributes making a wide turn around a curve" is both a specific attribute action and an accident-related action, the accident-related action identification unit 168 identifies "the action of a vehicle driven by a driver with specific attributes making a wide turn around a curve" as an action requiring attention.

[0123] The behavior presentation unit 170 performs processing to present the accident-related actions identified by the accident-related action identification unit 168 to the destination facility. The behavior presentation unit 170 also performs processing to present the actions requiring attention identified by the accident-related action identification unit 168 to the destination facility. Specifically, the behavior presentation unit 170 performs processing to transmit information regarding accident-related actions and actions requiring attention to the terminal device 40. The behavior presentation unit 170 may also control the communication unit 106 to transmit information regarding accident-related actions and actions requiring attention to the terminal device 40. Furthermore, the behavior presentation unit 170 may perform processing to transmit an instruction to the terminal device 40 to display the information regarding accident-related actions and actions requiring attention on the display of the terminal device 40.

[0124] The behavior presentation unit 170 then transmits information regarding accident-related actions and actions requiring attention to the terminal device 40, which then displays the information regarding accident-related actions and actions requiring attention. In this way, accident-related actions and actions requiring attention are presented to the receiving facility. By presenting accident-related actions and actions requiring attention to the receiving facility in this manner, the manager of the receiving facility can easily grasp these actions. Therefore, the receiving facility can provide appropriate driving guidance to drivers with specific characteristics. In other words, the receiving facility can provide particularly focused guidance to drivers with specific characteristics regarding actions that are unique to drivers with those characteristics and have a high probability of causing accidents.

[0125] For example, if the facility to which the information is presented is a driving school, the manager of the facility can plan the training so that the behavior requiring attention can be reproduced at the driving school. Then, if a driver with specific characteristics performs a behavior requiring attention while driving at the driving school, the facility's imaging device 50 may capture the scene. The manager can then allow the driver with specific characteristics who was driving to view the image of that scene, thereby enabling effective guidance for that driver. Consequently, it becomes possible to reduce the occurrence of accidents involving vehicles driven by drivers with specific characteristics.

[0126] Figure 17 is a flowchart illustrating an example of processing performed by the information processing device 100 according to this disclosure. Furthermore, Figure 17 shows an information processing method performed by the information processing device 100 according to this disclosure. It can also be said that Figure 17 shows an information presentation method performed by the information processing device 100 according to this disclosure.

[0127] The information processing device 100 performs substantially the same processing as described in S202 to S224 using Figure 13 (step S302). The behavior identification unit 160 identifies specific attribute actions, which are actions specific to drivers with specific attributes, as described above (step S312). The behavior identification unit 160 identifies accident-related actions, which are related to the cause of an accident by a driver with specific attributes, as described above (step S322). The behavior identification unit 160 also identifies actions that require attention, which are specific attribute actions and accident-related actions, as described above (step S324). The behavior presentation unit 170 performs processing to present the specific attribute actions, accident-related actions, actions that require attention, specific attribute actions, accident-related actions, and actions that require attention to the presentation destination facility, as described above (step S326).

[0128] (Example of Application) Figure 18 is a diagram showing an example of the application of the information presentation system 20 according to this disclosure. Figure 18 illustrates a facility 60 which is a driving school. At facility 60, driving instruction is provided to drivers with specific attributes. At facility 60, multiple facility-side imaging devices 50 are arranged on the driving course. As described above, the terminal device 40 displays specific attribute behavior, accident-related behavior, behavior requiring attention, specific attribute operation, accident-related operation, and behavior requiring attention, which are presented by the information processing device 100. The facility manager views the specific attribute behavior, accident-related behavior, behavior requiring attention, specific attribute operation, accident-related operation, and behavior requiring attention displayed on the terminal device 40. The manager then has the driver with specific attributes reproduce the specific attribute behavior, which is a behavior characteristic of the specific attribute, and the behavior requiring attention related to the driver with specific attributes at facility 60. Furthermore, the administrator will have the facility 60 recreate situations in which drivers with specific attributes may actually perform actions specific to drivers with those attributes, as well as actions requiring attention that are associated with drivers with those attributes.

[0129] The facility-side imaging device 50 then captures scenes of a vehicle driven by a driver with specific attributes traveling along the driving course of the facility 60 and transmits the obtained images to the terminal device 40. The terminal device 40 acquires behavioral data about the vehicle of the driver with specific attributes using the images obtained from the facility-side imaging device 50, in substantially the same manner as the processing of the driving data acquisition unit 130 described above. The terminal device 40 then presents the driver with specific attributes with data comparing the behavioral data about the driver with specific attributes with the data indicating the specific attribute behavior, accident-related behavior, behavior requiring attention, specific attribute action, accident-related action, and behavior requiring attention described above. This allows the driver with specific attributes to objectively understand whether their vehicle is exhibiting specific attribute behavior, accident-related behavior, behavior requiring attention, specific attribute action, accident-related action, and behavior requiring attention. Therefore, effective driving technique guidance can be provided to the driver with specific attributes.

[0130] For example, suppose "behavior related to turning" is identified as a behavior requiring attention, and "the vehicle making a wide turn around a curve" is identified as a behavior requiring attention. In this case, the administrator may have driver A, who has specific attributes, perform "behavior related to turning" in a situation where there is a possibility of "the vehicle making a wide turn around a curve". The facility's imaging device 50 then photographs the scene in which driver A's vehicle is performing "behavior related to turning" on the driving course of facility 60. The terminal device 40 uses the image of driver A's vehicle performing "behavior related to turning" to acquire driver A's behavioral data regarding "behavior related to turning". The terminal device 40 then presents driver A with data comparing driver A's behavioral data regarding "behavior related to turning" with driving data regarding "behavior related to turning". This allows driver A to objectively understand whether or not "the vehicle making a wide turn around a curve" is occurring when their vehicle is performing "behavior related to turning". Therefore, effective instruction on driving techniques can be provided to driver A.

[0131] In the example shown in Figure 18, the facility manager had a driver with specific attributes reproduce the aforementioned behaviors and actions on a real driving course, but the configuration is not limited to this. The facility 60 may also reproduce the aforementioned behaviors and actions in a virtual space such as AR (Augmented Reality), VR (Virtual Reality), or a simulator. In this case, for example, the manager may have a driver with specific attributes perform driving operations in the virtual space to produce the aforementioned behaviors. Alternatively, for example, the manager may play back video footage of the aforementioned behaviors and actions in the virtual space.

[0132] (Variations) The present invention is not limited to the embodiments described above, and can be modified as appropriate without departing from the spirit of the invention. For example, the order of each process in the flowchart described above can be changed as appropriate. Also, one or more of the processes in the flowchart described above may be omitted.

[0133] Furthermore, for example, each process in the flowchart described above may be implemented using a trained model learned by machine learning. In other words, the deviation degree determination unit 140 described above may use a trained model learned by machine learning to determine the degree of deviation between the overall driving data and the driving data relating to the vehicle of a driver with specific attributes. In this case, the trained model may be trained to take the overall driving data and the driving data relating to the vehicle of a driver with specific attributes as input and output the degree of deviation between the two.

[0134] Furthermore, Embodiment 2 and Embodiment 4 may be applied to each other. That is, in Embodiment 4, the accident information acquisition unit 150, the accident-related behavior identification unit 164, and the accident-related operation identification unit 168 may be omitted. In other words, when identifying specific attribute operations, it is not necessary to identify accident-related operations and operations requiring attention.

[0135] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure can be made as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0136] Each drawing is merely illustrative to illustrate one or more embodiments. Each drawing may be associated with one or more other embodiments, rather than being associated with only one specific embodiment. As those skilled in the art will understand, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings, for example, to create embodiments not explicitly shown or described. Not all features or steps shown in any one drawing to illustrate an exemplary embodiment are necessarily required, and some features or steps may be omitted. The order of steps described in any of the drawings may be changed as appropriate.

[0137] The program described above, when loaded into a computer, includes a set of instructions (or software code) for causing the computer to perform one or more of the functions described in the embodiments. The program may be stored in a non-temporary computer-readable medium or a physical storage medium. Examples, but not limited to, include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray® disc or other optical disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices. The program may be transmitted over a temporary computer-readable medium or a communication medium. Examples, but not limited to, include electrical, optical, acoustic or other forms of propagating signals. The program may also include a program product.

[0138] Although the present invention has been described above with reference to embodiments, the present invention is not limited thereto. Various modifications to the structure and details of the present invention can be made that are understandable to those skilled in the art within the scope of the invention.

[0139] Some or all of the above embodiments may also be described as follows, but are not limited to the following: (Note 1) An information processing device comprising: an image acquisition means for acquiring images obtained by photographing one or more locations on which a vehicle can travel; an attribute identification means for identifying the attributes of the driver of a vehicle shown in the image; a driving data acquisition means for acquiring driving data indicating the behavior of the vehicle at the location for each type of behavior for each of at least two driver attributes; a deviation degree determination means for determining whether the deviation degree is large between overall driving data, which is driving data for vehicles of drivers of at least two attributes, and driving data for vehicles of one or more drivers of a predetermined first attribute, using predetermined determination criteria; a behavior identification means for identifying the type of behavior for which the deviation degree is determined to be large as a first behavior, which is a characteristic behavior in the driving of a driver of the first attribute; and a presentation means for performing processing to present the identified first behavior. (Note 2) The information processing device according to Note 1, further comprising: accident information acquisition means for acquiring accident information relating to an accident involving a vehicle driven by a driver of the first attribute, wherein the behavior identification means identifies behavior relating to an accident involving a vehicle driven by a driver of the first attribute as a second behavior based on the accident information, and the presentation means performs processing for presenting the identified second behavior. (Note 3) The information processing device according to Note 2, wherein the behavior identification means identifies behavior associated with the type of accident involving a vehicle driven by a driver of the first attribute that has a high occurrence rate as the second behavior. (Note 4) The information processing device according to Note 2 or 3, wherein the behavior identification means identifies a behavior that is both the first behavior and the second behavior as a third behavior, and the presentation means performs processing for presenting the identified third behavior. (Note 5) The information processing device according to Note 1, wherein the behavior identification means identifies a first behavior that is specific to a driver of the first attribute in the first behavior.(Note 6) The information processing device according to Note 5, wherein the behavior identification means identifies an action as the first action when the frequency of an action performed by a driver with the first attribute in the first action is higher than the frequency of the action performed by a driver with the attribute corresponding to the overall driving data. (Note 7) The information processing device according to Note 5, wherein the behavior identification means identifies an action that deviates from a normal distribution obtained using driving data relating to the first action performed by a driver with the first attribute as the first action. (Note 8) The information processing device according to any one of Notes 5 to 7, further comprising: accident information acquisition means for acquiring accident information relating to an accident due to the first attribute; the behavior identification means identifies an action relating to the cause of the accident as a second action based on the accident information; the behavior identification means identifies an action that is both the first action and the second action as a third action; and the presentation means performs processing for presenting the identified third action. (Note 9) The information processing device according to Note 1, wherein the attribute identification means identifies the driver's attribute as the attribute indicated by the mark when the vehicle shown in the image is marked with a mark indicating the driver's attribute. (Note 10) The information processing device according to Note 1, wherein the attribute identification means identifies the driver's attribute based on the driver's face of the vehicle shown in the image. (Note 11) The information processing device according to Note 1, wherein the first attribute is elderly. (Note 12) The information processing device according to Note 1, wherein the deviation degree determination means determines, for each behavior, whether the deviation degree between the overall driving data and the driving data relating to the first attribute is large at each of the plurality of points, and the behavior identification means identifies the behavior for which the deviation degree is determined to be large at a predetermined number of points as the first behavior. (Note 13) The information processing device according to Note 1, wherein the behavior at the said point includes at least one of the following behaviors: behavior related to stopping, behavior related to turning, behavior related to going straight, behavior related to changing lanes, and behavior related to starting.(Note 14) The information processing apparatus according to Note 1, wherein the deviation degree determination means determines whether the deviation degree is large for each of the behaviors between the frequency distribution of the behavior shown in the overall driving data and the frequency distribution of the behavior shown in the driving data related to the first attribute. (Note 15) An information processing method comprising: an image acquisition means for acquiring images obtained by photographing one or more locations where a vehicle can travel; an information processing method for identifying the attributes of the driver of a vehicle shown in the image; acquiring driving data indicating the behavior of the vehicle at the location for each type of behavior for each of at least two driver attributes; determining, using predetermined criteria, whether the degree of discrepancy is large between the overall driving data, which is the driving data for the vehicles of drivers with at least two attributes, and the driving data for the vehicles of one or more drivers with a predetermined first attribute; identifying the type of behavior for which the degree of discrepancy is determined to be large as a first behavior, which is a characteristic behavior of the driving of the driver with the first attribute; and performing processing to present the identified first behavior. (Note 16) A non-temporary computer-readable medium storing a program that causes a computer to execute: an image acquisition means for acquiring images obtained by photographing one or more locations where a vehicle can travel; a step of identifying the attributes of the driver of the vehicle shown in the image; a step of acquiring driving data indicating the behavior of the vehicle at the location for each type of behavior for each of at least two driver attributes; a step of determining, using predetermined criteria, whether the degree of discrepancy between the overall driving data, which is driving data for the vehicles of drivers of at least two attributes, and the driving data for the vehicles of one or more drivers of a predetermined first attribute is large; a step of identifying the type of behavior for which the degree of discrepancy is determined to be large as a first behavior, which is a characteristic behavior of the driving of the driver of the first attribute; and a step of processing for presenting the identified first behavior.

[0140] Some or all of the elements (e.g., configuration and function) described in Appendices 2 to 14 that are dependent on Appendice 1 may also be dependent on Appendices 15 and 16 in the same way as in Appendices 2 to 14. Some or all of the elements described in any appendice may be applied to various hardware, software, recording means, systems, and methods for recording software.

[0141] 1 Information Processing Device 2 Image Acquisition Unit 4 Attribute Identification Unit 6 Driving Data Acquisition Unit 8 Degree of Deviation Determination Unit 10 Behavior Identification Unit 12 Presentation Unit 20 Information Presentation System 30 Roadside Imaging Device 40 Terminal Device 50 Facility-side Imaging Device 100 Information Processing Device 120 Image Acquisition Unit 122 Attribute Identification Unit 124 Trained Model Storage Unit 130 Driving Data Acquisition Unit 132 Driving Data Storage Unit 140 Degree of Deviation Determination Unit 150 Accident Information Acquisition Unit 160 Behavior Identification Unit 162 Specific Attribute Behavior Identification Unit 164 Accident-Related Behavior Identification Unit 166 Specific Attribute Operation Identification Unit 168 Accident-Related Operation Identification Unit 170 Behavior Presentation Unit

Claims

1. An information processing device comprising: an image acquisition means for acquiring images obtained by photographing one or more locations where a vehicle can travel; an attribute identification means for identifying the attributes of the driver of a vehicle shown in the image; a driving data acquisition means for acquiring driving data indicating the behavior of the vehicle at the location for each type of behavior for each of at least two driver attributes; a deviation degree determination means for determining whether the deviation degree is large between overall driving data, which is driving data for vehicles of drivers with at least two attributes, and driving data for vehicles of one or more drivers of a predetermined first attribute, using predetermined determination criteria; a behavior identification means for identifying the type of behavior for which the deviation degree is determined to be large as a first behavior, which is a characteristic behavior of the driving of the driver of the first attribute; and a presentation means for performing processing to present the identified first behavior.

2. An information processing device according to claim 1, further comprising: an accident information acquisition means for acquiring accident information relating to an accident involving a vehicle driven by a driver having the first attribute, wherein the behavior identification means identifies the behavior relating to the accident involving a vehicle driven by a driver having the first attribute as a second behavior based on the accident information, and the presentation means performs processing for presenting the identified second behavior.

3. The information processing apparatus according to claim 2, wherein the behavior identification means identifies as the second behavior the behavior associated with the type of accidents that occur most frequently among the types of accidents involving a vehicle driven by a driver of the first attribute.

4. The information processing apparatus according to claim 2 or 3, wherein the behavior identification means identifies the behavior which is both the first behavior and the second behavior as a third behavior, and the presentation means performs processing for presenting the identified third behavior.

5. The information processing apparatus according to claim 1, wherein the behavior identification means identifies a first action which is an action specific to a driver of the first attribute in the first behavior.

6. The information processing apparatus according to claim 5, wherein the behavior identification means identifies an action as the first action when the frequency of an action performed by a driver with the first attribute in the first behavior is higher than the frequency of such action performed by a driver with the attribute corresponding to the overall driving data.

7. The information processing apparatus according to claim 5, wherein the behavior identification means identifies an action that deviates from a normal distribution obtained using driving data relating to the first behavior by a driver of the first attribute as the first action.

8. An information processing device according to any one of claims 5 to 7, further comprising: an accident information acquisition means for acquiring accident information relating to an accident based on the first attribute; the behavior identification means for identifying an action relating to the cause of the accident as a second action based on the accident information; the behavior identification means for identifying an action that is both the first action and the second action as a third action; and the presentation means for performing a process for presenting the identified third action.

9. The information processing apparatus according to claim 1, wherein the attribute identification means identifies the attribute of the driver as the attribute indicated by the mark when a mark indicating the driver's attribute is attached to the vehicle shown in the image.

10. The information processing apparatus according to claim 1, wherein the attribute identification means identifies the attributes of the driver based on the face of the vehicle driver shown in the image.

11. The information processing apparatus according to claim 1, wherein the first attribute is being elderly.

12. The information processing apparatus according to claim 1, wherein the deviation degree determination means determines, for each of the plurality of points, whether the deviation degree between the overall driving data and the driving data relating to the first attribute is large, and the behavior identification means identifies the behavior for which the deviation degree is determined to be large at a predetermined number of points as the first behavior.

13. The information processing apparatus according to claim 1, wherein the behavior at the point includes at least one of the following behaviors: behavior related to stopping, behavior related to turning, behavior related to going straight, behavior related to changing lanes, and behavior related to starting.

14. The information processing apparatus according to claim 1, wherein the deviation degree determination means determines whether the deviation degree is large for each of the behaviors between the frequency distribution of the behavior shown in the overall driving data and the frequency distribution of the behavior shown in the driving data relating to the first attribute.

15. An information processing method comprising: an image acquisition means for acquiring images obtained by photographing one or more locations where a vehicle can travel; an information processing method for identifying the attributes of the driver of a vehicle shown in the image; acquiring driving data indicating the vehicle's behavior at the location for each type of behavior for at least two driver attributes; determining, using predetermined criteria, whether the degree of discrepancy between the overall driving data, which is the driving data for the vehicles of at least two drivers with certain attributes, and the driving data for the vehicles of one or more drivers with a predetermined first attribute is large; identifying the type of behavior determined to have a large degree of discrepancy as a first behavior, which is a characteristic behavior of the driver with the first attribute; and performing processing to present the identified first behavior.

16. A non-temporary computer-readable medium storing a program that causes a computer to execute: an image acquisition means for acquiring images obtained by photographing one or more locations where a vehicle can travel; a step of identifying the attributes of the driver of the vehicle shown in the image; a step of acquiring driving data indicating the behavior of the vehicle at the location for each type of behavior for each of at least two driver attributes; a step of determining, using predetermined criteria, whether the degree of discrepancy is large between the overall driving data, which is the driving data for the vehicles of at least two drivers with attributes, and the driving data for the vehicles of one or more drivers with a predetermined first attribute; a step of identifying the type of behavior for which the degree of discrepancy is determined to be large as a first behavior, which is a characteristic behavior of the driving of the driver with the first attribute; and a step of performing processing to present the identified first behavior.

Images