Action evaluation device and action evaluation method

Abstract

An action evaluation device (100) comprises an action acquisition unit (111) that acquires the action of an occupant driving a mobile body, a position acquisition unit (112) that acquires the position of the mobile body, a model acquisition unit (113) that acquires an exemplary behavior model corresponding to a road environment at the position of the mobile body by using a database in which position information and an exemplary behavior model indicating an exemplary action corresponding to the road environment are stored in advance in association with each other, and an evaluation value calculation unit (114) that calculates an evaluation value of the action on the basis of the action acquired by the action acquisition unit and the exemplary behavior model acquired by the model acquisition unit.

Description

Movement evaluation device and movement evaluation method

[0001] The disclosed technology relates to a motion evaluation technology for evaluating the motion of a driver of a vehicle.

[0002] In recent years, technologies have been developed to encourage occupants of moving objects to perform exemplary driving. Patent Document 1 discloses a technology for evaluating the driving performance of a vehicle driver. The technology described in Patent Document 1 uses information on devices acquired by sensors to create a database of operation data of devices related to the vehicle's driving performance for each location as time-series data when an exemplary driving performance is assumed, and evaluates the driving performance using the database based on the difference between operation data related to actual driving performance and operation data related to exemplary driving performance (paragraphs

[0016] ,

[0020] ,

[0021] ,

[0024] , etc., of Patent Document 1).

[0003] JP 2009-294250 A

[0004] However, depending on the environment of the road on which the vehicle is traveling (road environment), it may be preferable for the driver to perform the operation before actually performing the driving operation, such as by checking the surrounding situation in order to collect information necessary for the driving operation. The technology described in Patent Document 1 has a problem in that it cannot perform evaluation based on actions other than the driving operation estimated from the state of the devices related to the driving operation, and therefore cannot perform a more preferable driving evaluation.

[0005] The present disclosure is intended to solve the above-mentioned problems, and aims to enable a more favorable driving evaluation than conventional methods.

[0006] The action evaluation device disclosed herein includes: an action acquisition unit that acquires actions of an occupant driving a mobile body; a position acquisition unit that acquires the position of the mobile body; a model acquisition unit that acquires an example behavior model that corresponds to the road environment at the position of the mobile body using a database that associates position information with an example behavior model that shows example behaviors that correspond to the road environment and that has been stored in advance; and an evaluation value calculation unit that calculates an evaluation value of the action based on the action acquired by the action acquisition unit and the example behavior model acquired by the model acquisition unit.

[0007] According to the present disclosure, an effect is achieved in that a more preferable driving evaluation can be performed compared to the conventional art.

[0008] FIG. 1 is a diagram illustrating a configuration example of an action evaluating device according to a first embodiment of the present disclosure. FIG. 2 is a diagram illustrating a configuration example of an action evaluating system including the action evaluating device according to the first embodiment of the present disclosure. FIG. 3 is a flowchart illustrating an example of processing by the action evaluating device according to the first embodiment of the present disclosure. FIG. 4 is a diagram illustrating a configuration example of an action evaluating system including the action evaluating device according to the second embodiment of the present disclosure. FIG. 5 is a flowchart illustrating an example of processing by the action evaluating device according to the second embodiment of the present disclosure. FIG. 6 is a diagram illustrating a configuration example of an action evaluating system including the action evaluating device according to the third embodiment of the present disclosure. FIG. 7 is a flowchart illustrating an example of processing by the action evaluating device according to the third embodiment of the present disclosure. FIG. 8 is a diagram illustrating a configuration example of an action evaluating system including the action evaluating device according to the fourth embodiment of the present disclosure. FIG. 9 is a first diagram illustrating an example of accumulated inattentive behavior handled by the action evaluating device according to the fourth embodiment. FIG. 10 is a second diagram illustrating an example of accumulated inattentive behavior handled by the action evaluating device according to the fourth embodiment. FIG. 11 is a flowchart illustrating an example of processing by the action evaluating device according to the fourth embodiment of the present disclosure. FIG. 12 is a flowchart illustrating an example of processing by a model acquiring unit in the action evaluating device according to the fourth embodiment of the present disclosure. FIG. 13 is a diagram illustrating a relationship between an action evaluating apparatus according to embodiment 5 of the present disclosure and a server apparatus that generates a database used in the action evaluating apparatus. FIG. 14 is a diagram illustrating a first configuration example of an action evaluating system E including an action evaluating apparatus according to embodiment 5 of the present disclosure. FIG. 15 is a diagram illustrating a second configuration example of an action evaluating system including an action evaluating apparatus according to embodiment 5 of the present disclosure. FIG. 16 is a diagram illustrating a third configuration example of an action evaluating system including an action evaluating apparatus according to embodiment 5 of the present disclosure. FIG. 17 is a flowchart illustrating an example of processing by a action information providing unit in the action evaluating apparatus according to embodiment 5 of the present disclosure. FIG. 18 is a flowchart illustrating an example of processing by a server apparatus when action information is provided from the action evaluating apparatus according to embodiment 5 of the present disclosure. FIG. 19 is a diagram illustrating a first example of a hardware configuration for realizing functions according to the configuration of the present disclosure. FIG. 20 is a diagram illustrating a second example of a hardware configuration for realizing functions according to the configuration of the present disclosure.

[0009] In order to explain the present disclosure in more detail, embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First Embodiment In the first embodiment, a basic form of the present disclosure will be described.

[0011] An example configuration of a motion evaluation device will be described. FIG. 1 is a diagram illustrating an example configuration of a motion evaluation device 100 according to a first embodiment of the present disclosure. The motion evaluation device 100 evaluates the motion of an occupant aboard a moving body. The moving body is a vehicle or the like. The motion evaluation device 100 evaluates whether the motion of the occupant deviates from a pre-stored exemplary motion. The motion evaluation device 100 illustrated in FIG. 1 includes a motion acquisition unit 111, a position acquisition unit 112, a model acquisition unit 113, and an evaluation value calculation unit 114.

[0012] The action acquisition unit 111 acquires the actions of the occupant driving the vehicle. The action acquisition unit 111 acquires the actions themselves performed by the occupant. The actions acquired by the action acquisition unit 111 are, for example, the actions of the occupant determined using video of the interior of the vehicle. This makes it possible to acquire both the actions before and after the driving operation, compared to conventional driving operations that are inferred from the state of the equipment. In this case, the actions that can be acquired by the action acquisition unit 111 are represented in the form of, for example, the direction in which the occupant's face is facing, the direction in which the occupant's line of sight, the position of the occupant's hands, change values ​​indicating the degree to which each of these has changed over time, and the time indicating how long each of these has remained constant.

[0013] The position acquisition unit 112 acquires the position of the mobile body. The position acquisition unit 112 acquires the position in chronological order from, for example, at least one of a GPS (Global Positioning System) device and a car navigation device mounted on the mobile body. The position acquisition unit 112 can output operation information, which is a set of information indicating the acquired position and information indicating the operation acquired by the operation acquisition unit 111.

[0014] The model acquisition unit 113 acquires an exemplary behavior model that indicates exemplary behavior according to the road environment at the position acquired by the position acquisition unit 112. For example, the model acquisition unit 113 acquires an exemplary behavior model according to the road environment at the position of the mobile object using a database that associates and pre-stores position information with exemplary behavior models that indicate exemplary behavior according to the road environment. The "road environment" refers to the environment around the road in which an occupant must perform an action before actually driving the mobile object. When the mobile object is a vehicle, the road environment includes at least one of a motorway, a merging area of ​​the motorway, an intersection, a crosswalk, the presence or absence of a traffic light, the shape of the road, the width of the road, the degree of vehicle congestion, or the degree of pedestrian congestion. The motorway is, for example, an expressway. The merging area of ​​the motorway is, for example, an area including an acceleration lane located at the merging point of a main lane of the expressway and a merging lane that merges into the main lane. For example, "near an intersection" refers to an environment in which an intersection exists in the direction of travel of the vehicle based on the actual position of the vehicle. "near a crosswalk" refers to an environment in which a crosswalk exists in the direction of travel of the vehicle based on the actual position of the vehicle. "Presence or absence of a traffic light" refers to an environment in which a traffic light exists in the direction of travel of the vehicle based on the actual position of the vehicle. "Road shape" refers to an environment in which the road in the direction of travel of the vehicle is curved based on the actual position of the vehicle. "Road width" refers to an environment in which the width of the road in the direction of travel of the vehicle is wide based on the actual position of the vehicle. "Degree of vehicle congestion" refers to the number of vehicles traveling on the road in the direction of travel of the vehicle based on the actual position of the vehicle. When there are many vehicles traveling on the road (e.g., in the case of a traffic jam), it is possible to evaluate the behavior of the occupant, such as the driver, based on the idea that it is better for the occupant to prepare to press the brake pedal than when there are few vehicles traveling on the road. The degree of pedestrian congestion indicates the number of people walking around the road in the direction of travel of the vehicle, based on the actual position of the vehicle.When there are many pedestrians around the roadway and the roadway is narrow, it is possible to evaluate the behavior of an occupant, such as a driver, based on the idea that it is better for the occupant to observe the sidewalk. The "model behavior model" is information indicating model behavior according to the road environment. The model behavior model includes at least one of observation direction, cumulative inattentiveness, and body part position. The "observation direction" is the direction in which the occupant is likely to observe. The "observation direction" includes at least one of the direction in which the occupant's face is facing (face orientation) or the occupant's line of sight. By including this behavior in the model behavior model, for example, when the mobile object is located in a road environment near a crosswalk, it becomes possible to evaluate whether the occupant is observing the sidewalk. Furthermore, when a road sign is present diagonally above and ahead of the mobile object, it becomes possible to evaluate whether the occupant is observing the direction in which the road sign is located. "Cumulative inattentiveness" refers to an inattentive behavior based on a cumulative value of intermittent inattentiveness by a vehicle occupant. For example, this behavior occurs when the cumulative value reaches or exceeds a threshold value over a predetermined time period. Including this behavior in the exemplary behavior model makes it possible to evaluate, for example, whether the occupant, when present in a merging area of ​​a highway, sufficiently alternately observes the area in front of the vehicle and the area behind the merging road before merging. "Body part position" includes, for example, the angle of the hands relative to the steering wheel. Including this behavior in the exemplary behavior model makes it possible to evaluate, for example, whether the occupant is prepared to operate a turn signal before entering an intersection when the vehicle is turning right or left at an intersection. The exemplary behavior model may also include driving operations performed on the vehicle. Driving operations include, for example, operations such as accelerating or decelerating the vehicle's speed and turning on or off a turn signal.

[0015] The evaluation value calculation unit 114 calculates an evaluation value of the occupant's action. The evaluation value calculation unit 114 calculates an evaluation value of the action based on the action acquired by the action acquisition unit 111 and the exemplary behavior model acquired by the model acquisition unit 113. A known technique can be used to calculate the evaluation value. For example, the evaluation value calculation unit 114 compares the action acquired by the action acquisition unit 111 with the exemplary behavior model acquired by the model acquisition unit 113 to calculate a difference value, and uses the difference value to derive an evaluation value indicating the degree of deviation of the actual action from the exemplary behavior model. The evaluation value in this case can also be expressed as a deviation degree. Alternatively, it can also be expressed as a driving degree.

[0016] In addition to the above components, the action evaluation device 100 also includes a control unit (not shown), a storage unit (not shown), and a communication unit (not shown). The control unit (not shown) controls the entire action evaluation device 100 and each of its components. The control unit (not shown), for example, starts up the action evaluation device 100 in response to an external command. The control unit (not shown) also controls the state of the action evaluation device 100 (operation state = startup, shutdown, sleep, etc.). The storage unit (not shown) stores various data used in the action evaluation device 100. For example, the storage unit (not shown) stores output (output data) from each component of the action evaluation device 100 and outputs data requested by each component to the requesting component. The communication unit (not shown) communicates with external devices. For example, communication is performed between the action evaluation device 100 (100A) and peripheral devices (the occupant monitoring device 300A and the server device 600E, which will be described later). For example, when the action evaluation device 100 and the occupant monitoring device are not connected by wire, a communication unit (not shown) has a function of communicating between the action evaluation device 100 and the occupant monitoring device 300A. The communication unit (not shown) also has a function of communicating with a server device 600E, which is an external device. The control unit (not shown), the storage unit (not shown), and the communication unit (not shown) each have the same functions in the embodiments described below.

[0017] Next, a configuration example of an action evaluation system including an action evaluation device will be described. Fig. 2 is a diagram showing a configuration example of an action evaluation system 1A including an action evaluation device 100A according to the first embodiment of the present disclosure. The action evaluation system 1A shown in Fig. 2 is configured to include the action evaluation device 100A, a model behavior database 200A (database), an occupant monitoring device 300A, and an output device 400A. Details of the occupant monitoring device 300A, the model behavior database 200A (database), the action evaluation device 100A, and the output device 400A will be described in order.

[0018] The occupant monitoring device 300A is a device that monitors the state of an occupant, and is used, for example, in a DMS (Driver Monitoring System). The occupant monitoring device 300A includes a video acquisition unit 301A and a motion determination unit 302A.

[0019] The image acquisition unit 301A acquires an image of the interior of the mobile object from, for example, an imaging device that captures images of the interior of the mobile object.

[0020] The movement determination unit 302A determines the movement of the occupant captured in the video acquired by the video acquisition unit 301A. The movement determination unit 302A acquires the video from the video acquisition unit 301A and determines the movement of the occupant using the acquired video. Note that the technique for determining the movement of the occupant can be realized using known technology.

[0021] The occupant monitoring device 300A outputs information indicating the occupant's movement determined by the movement determining unit 302A to the movement evaluating device 100A.

[0022] The model behavior database 200A (database) is a database that stores in advance location information and model behavior models in association with each other. The model behavior database 200A (database) may be a database that stores location information and model behavior models in association with each other, or may be a database that stores location information, road environment information indicating the road environment, and model behavior models in association with each other. The location information is information in the form of, for example, coordinate values ​​of map data. The location information is not limited to a single point, but may also be information indicating an area. The road environment information is information that indicates the road environment so that the above-mentioned types of road environment can be distinguished from each other.

[0023] 2 is communicably connected to an occupant monitoring device 300A, an exemplary behavior database 200A (database), and an output device 400A. The action evaluation device 100A includes a action acquisition unit 111A, a position acquisition unit 112A, a model acquisition unit 113A, and an evaluation value calculation unit 114A.

[0024] The motion acquisition unit 111A has the same functions as the motion acquisition unit 111 already described, and detailed description thereof will be omitted. The motion acquisition unit 111A acquires the motions of the occupant output by the occupant monitoring device 300A and uses them for processing.

[0025] The position acquisition unit 112A has the same functions as the position acquisition unit 112 already described, and a detailed description thereof will be omitted.

[0026] The model acquisition unit 113A has the same functions as the model acquisition unit 113 already described, and detailed description thereof will be omitted. The model acquisition unit 113A refers to the model behavior database 200A to acquire a model behavior model corresponding to the road environment at the location of the mobile object.

[0027] The evaluation value calculation unit 114A has the same function as the evaluation value calculation unit 114 already described, and detailed description thereof will be omitted. The evaluation value calculation unit 114A outputs the calculated evaluation value to the output device 400A.

[0028] The output device 400A has at least one of a display function, a sound output function, and a vibration output function. The output device 400A shown in Fig. 2 outputs information based on information received from the action evaluation device 100A. For example, the output device 400A displays an image including an evaluation value upon receiving a command from the action evaluation device 100A to display an image including an evaluation value.

[0029] An example of processing by the action evaluating device will be described. Since the processing by the action evaluating device 100 and the action evaluating device 100A is similar, an example of processing by the action evaluating device 100A will be described here as a representative. FIG. 3 is a flowchart showing an example of processing by the action evaluating device 100A according to the first embodiment of the present disclosure. The action evaluating device 100A starts the processing shown in FIG. 3 upon receiving a command from a control unit (not shown) or an external command. The action evaluating device 100A starts the processing when, for example, operation of a mobile object is started.

[0030] The action evaluation device 100A first executes a action acquisition process (step ST1110). In the action acquisition process, the action acquisition unit 111A of the action evaluation device 100A acquires the action of the occupant driving the vehicle. The action acquisition unit 111A acquires information related to the action output from the occupant monitoring device 300A and outputs the information to the position acquisition unit 112A.

[0031] Next, the action evaluating device 100A executes a position acquisition process (step ST1120). In the position acquisition process, the position acquisition unit 112A of the action evaluating device 100A acquires the position of the moving object. Upon acquiring the information related to the action output by the action acquisition unit 111A, the position acquisition unit 112A acquires position information indicating the position of the moving object. The position acquisition unit 112A outputs action information, which is a set of the position information and the information related to the action, to the model acquisition unit 113A.

[0032] The action evaluation device 100A then executes a model acquisition process (step ST1130). In the model acquisition process, the model acquisition unit 113A of the action evaluation device 100A acquires an exemplary behavior model that shows exemplary behavior corresponding to the road environment at the position acquired by the position acquisition unit 112A. Upon acquiring the action information output by the position acquisition unit 112A, the model acquisition unit 113A refers to the exemplary behavior database 200A using the position information included in the action information to acquire an exemplary behavior model corresponding to the road environment at the position of the mobile object. The model acquisition unit 113A outputs the action information and the exemplary behavior model to the evaluation value calculation unit 114A.

[0033] The action evaluation device 100A then executes an evaluation value calculation process (step ST1140). In the evaluation value calculation process, the evaluation value calculation unit 114A of the action evaluation device 100A calculates an evaluation value for the action based on the action acquired by the action acquisition unit 111 and the exemplary behavior model acquired by the model acquisition unit 113A. Specifically, after acquiring, for example, the action information and the exemplary behavior model output by the model acquisition unit 113A, the evaluation value calculation unit 114A compares the information related to the action included in the action information with the exemplary behavior model to calculate a difference value. The evaluation value calculation unit 114A uses the difference value to calculate an evaluation value (deviation) indicating the degree of deviation of the actual action from the exemplary behavior model. The evaluation value calculation unit 114A outputs the evaluation value as a calculation result to the output device 400A. As a result, the output device 400A displays the evaluation value.

[0034] The action evaluation device 100A then executes an end determination process (step ST1150). In the end determination process, a control unit (not shown) of the action evaluation device 100A determines whether to end the processing of the action evaluation device 100A. The control unit (not shown) determines whether to end the processing of the action evaluation device 100A, for example, in accordance with an external end command or an execution program. If the control unit (not shown) determines not to end the processing of the action evaluation device 100A, the process proceeds to step ST1110, and processing is repeated from step ST1110. If the control unit (not shown) determines to end the processing of the action evaluation device 100A, the action evaluation device 100A ends the processing.

[0035] The above-described disclosure enables a more favorable driving evaluation than in the past. For example, it is possible to evaluate the actions that an occupant should perform before actually performing a driving operation on a moving object. Furthermore, for example, it is possible to change the evaluation of the occupant's actions depending on the road environment at the location of the moving object.

[0036] The action evaluation device of the present disclosure is configured, for example, as follows: An action evaluation device including: an action acquisition unit that acquires actions of an occupant driving a mobile object; a position acquisition unit that acquires the position of the mobile object; a model acquisition unit that acquires an exemplary behavior model corresponding to the road environment at the position of the mobile object using a database that associates position information with an exemplary behavior model showing exemplary actions corresponding to the road environment and stores the association between the information and the exemplary behavior model; and an evaluation value calculation unit that calculates an evaluation value of the action based on the action acquired by the action acquisition unit and the exemplary behavior model acquired by the model acquisition unit. This makes it possible to provide an action evaluation device that performs a more favorable driving evaluation than conventional devices.

[0037] The action evaluation method of the present disclosure is configured, for example, as follows: A action evaluation method comprising: a action acquisition step in which a action acquisition unit of a action evaluation device acquires actions of an occupant driving a mobile object; a position acquisition step in which a position acquisition unit of the action evaluation device acquires the position of the mobile object; a model acquisition step in which a model acquisition unit of the action evaluation device acquires an exemplary behavior model corresponding to the road environment at the position of the mobile object using a database in which position information is associated with an exemplary behavior model showing exemplary actions corresponding to the road environment and is stored in advance; and an evaluation value calculation step in which an evaluation value calculation unit of the action evaluation device calculates an evaluation value of the action based on the action acquired by the action acquisition unit and the exemplary behavior model. This makes it possible to provide an action evaluation method that performs a more preferable driving evaluation than conventional methods.

[0038] The motion evaluation device of the present disclosure may further be configured, for example, as follows: The motion acquired by the motion acquisition unit is a motion of the occupant determined using a video of the interior of the vehicle. This provides an advantage over conventional methods of evaluating various occupant motions that are not directly reflected in driving operations. Furthermore, the present disclosure provides an advantage similar to the above-described advantage by applying the above configuration to a motion evaluation system or the above-described motion evaluation method.

[0039] The action evaluation device of the present disclosure may further be configured, for example, as follows: The action evaluation device is characterized in that the road environment includes at least one of a motorway, a merging area of ​​a motorway, an area near an intersection, an area near a pedestrian crossing, the presence or absence of a traffic light, the shape of the road, the width of the road, the degree of vehicle congestion, or the degree of pedestrian congestion. This provides an effect of making it possible to evaluate the actions of an occupant at a point where desirable actions are likely to occur before an actual driving operation. Furthermore, the present disclosure provides the same effect as the above by applying the above configuration to a action evaluation system or the above action evaluation method.

[0040] The action evaluation device of the present disclosure may further be configured, for example, as follows: The action evaluation device is characterized in that the model behavior model includes at least one of observation direction, cumulative inattentiveness, or body part position. This provides an effect, for example, of being able to evaluate whether the occupant's observation direction is facing a direction that allows information necessary for driving operation to be collected. Alternatively, it provides an effect, for example, of being able to evaluate whether the occupant's body part is in a position that allows preparation for driving operation. Furthermore, the present disclosure provides the same effect as the above by applying the above configuration to the action evaluation system or the above action evaluation method.

[0041] The action evaluation device of the present disclosure may further be configured, for example, as follows: The observation direction includes at least one of the face direction of the occupant or the line of sight of the occupant. This provides an effect of further improving the accuracy of evaluating the observation direction. Furthermore, the present disclosure provides the same effect as the above by applying the above configuration to the action evaluation system or the action evaluation method.

[0042] Embodiment 2. In embodiment 2, a configuration is described in which a component for determining a motion is included within the motion evaluation device. Also, in embodiment 2, a configuration is described in which a component for determining whether to issue a warning to an occupant is included within the motion evaluation device. In embodiment 2, duplicated descriptions of components according to embodiment 2 that are the same as those according to embodiment 1 already described will be omitted as appropriate.

[0043] A configuration example of a movement evaluation device and a movement evaluation system including the device according to a second embodiment of the present disclosure will be described. FIG. 4 is a diagram showing a configuration example of a movement evaluation system 1B including a movement evaluation device 100B according to the second embodiment of the present disclosure. The movement evaluation system 1B is configured to include a movement evaluation device 100B, a model behavior database 200B (database), an output device 400B, and an imaging device 500B. The model behavior database 200B (database) is similar to the model behavior database 200A already described, and the output device 400B is similar to the output device 400A already described, so a redundant description of each will be omitted. Details of the imaging device 500B and the movement evaluation device 100B will be described in that order.

[0044] Imaging device 500B captures an image of the interior of the moving object and outputs the captured image. If the moving object is a vehicle, imaging device 500B is an in-vehicle camera for capturing an image of the interior of the vehicle.

[0045] The action evaluation device 100B includes components similar to those of the action evaluation devices 100 and 100A already described. In addition, the action evaluation device 100B includes a component for performing action determination. The action evaluation device 100B also includes a component for determining whether to issue a warning to the occupant. The action evaluation device 100B shown in FIG. 4 includes an image acquisition unit 121B, a action determination unit 122B, a action acquisition unit 111B, a position acquisition unit 112B, a model acquisition unit 113B, an evaluation value calculation unit 114B, and a warning unit 123B. The position acquisition unit 112B is similar to the position acquisition units 112 and 112A already described, and the model acquisition unit 113B is similar to the model acquisition units 113 and 113A already described, and therefore, description of each will be omitted to avoid duplication.

[0046] The image acquisition unit 121B acquires an image of the interior of the mobile object. Specifically, the image acquisition unit 121B acquires an image from an imaging device 500B that captures an image of the interior of the mobile object.

[0047] The movement determination unit 122B determines the movement of the occupant captured in the video acquired by the video acquisition unit 121B. Specifically, the movement determination unit 122B acquires the video from the video acquisition unit 121B and determines the movement of the occupant using the acquired video. The technique for determining the movement of the occupant can be realized using known technology.

[0048] The motion acquisition unit 111B has the same functions as the already-described motion acquisition unit 111. The motion acquisition unit 111B acquires information related to the motion of the occupant output by the motion determination unit 122B and uses the information for processing.

[0049] The evaluation value calculation unit 114B has a function of calculating an evaluation value similar to the already-described evaluation value calculation unit 114. The evaluation value calculation unit 114B outputs the calculated evaluation value to the warning unit 123B.

[0050] The warning unit 123B issues a warning to the occupant using the evaluation value. Specifically, the warning unit 123B compares the evaluation value with a pre-stored evaluation threshold and determines whether the evaluation value is equal to or greater than the evaluation threshold. For example, when the evaluation value is a deviation degree and the evaluation threshold is a deviation threshold, the warning unit 123B determines whether the deviation degree is equal to or greater than the deviation threshold. When the determination result indicates that the occupant's behavior deviates from the model behavior indicated in the model behavior model according to the road environment, the warning unit 123B instructs the output device 400B to issue a warning. The warning is issued, for example, by at least one of an image display, sound output, or vibration output.

[0051] An example of processing by the action evaluating device according to the second embodiment of the present disclosure will be described. Fig. 5 is a flowchart showing an example of processing by the action evaluating device 100B according to the second embodiment of the present disclosure. The action evaluating device 100B starts the processing shown in Fig. 5 upon receiving a command from a control unit (not shown) or an external command. The action evaluating device 100B starts the processing when, for example, operation of a moving object is started.

[0052] First, the action evaluating device 100B executes a video acquisition process (step ST2110). In the video acquisition process, the video acquiring unit 121B of the action evaluating device 100B acquires video from the imaging device 500 that captures images of the interior of the moving object. The video acquiring unit 121B outputs the acquired video to the action determining unit 122B.

[0053] Next, the action evaluation device 100B executes a action determination process (step ST2120). In the action determination process, the action determination unit 122B of the action evaluation device 100B determines the action of the occupant captured in the video acquired by the video acquisition unit 121B. The action determination unit 122B outputs information related to the action of the occupant, which is the determination result, to the action acquisition unit 111B.

[0054] Next, the action evaluation device 100B executes a action acquisition process (step ST2130). In the action acquisition process, the action acquisition unit 111B of the action evaluation device 100B acquires the action of the occupant driving the mobile object. The action acquisition unit 111B acquires information related to the action output from the action determination unit 122B and outputs the information to the position acquisition unit 112B.

[0055] Next, the action evaluating device 100B executes a position acquisition process (step ST2140). In the position acquisition process, the position acquisition unit 112B of the action evaluating device 100B acquires the position of the moving object. Upon acquiring the information related to the action output by the action acquisition unit 111B, the position acquisition unit 112B acquires position information indicating the position of the moving object. The position acquisition unit 112B outputs action information, which is a set of the position information and the information related to the action, to the model acquisition unit 113B.

[0056] The action evaluation device 100B then executes a model acquisition process (step ST2150). In the model acquisition process, the model acquisition unit 113B of the action evaluation device 100B acquires an exemplary behavior model that shows exemplary behavior according to the road environment at the position acquired by the position acquisition unit 112A. Upon acquiring the action information output by the position acquisition unit 112B, the model acquisition unit 113B refers to the exemplary behavior database 200B using the position information included in the action information to acquire an exemplary behavior model according to the road environment at the position of the mobile object. The model acquisition unit 113B outputs the action information and the exemplary behavior model to the evaluation value calculation unit 114B.

[0057] The action evaluation device 100B then executes an evaluation value calculation process (step ST2160). In the evaluation value calculation process, the evaluation value calculation unit 114B of the action evaluation device 100B calculates an evaluation value for the action based on the action acquired by the action acquisition unit 111 and the exemplary behavior model acquired by the model acquisition unit 113B. Specifically, upon acquiring, for example, action information and the exemplary behavior model output by the model acquisition unit 113B, the evaluation value calculation unit 114B compares information related to the action included in the action information with the exemplary behavior model to calculate a difference value. The evaluation value calculation unit 114B uses the difference value to calculate an evaluation value (deviation degree) indicating the degree of deviation of the actual action from the exemplary behavior model. The evaluation value calculation unit 114B outputs the evaluation value as a calculation result to the warning unit 123B.

[0058] The action evaluation device 100B then executes an evaluation value determination process ("evaluation value≧evaluation threshold", step ST2170). In the evaluation value determination process, the warning unit 123B of the action evaluation device 100B compares the evaluation value with the evaluation threshold. The warning unit 123B determines whether the evaluation value is equal to or greater than the evaluation threshold. Specifically, when the evaluation value is a deviation and the evaluation threshold is the deviation threshold, the warning unit 123B determines whether the deviation is equal to or greater than the deviation threshold.

[0059] When the warning unit 123B determines that the evaluation value is equal to or greater than the evaluation threshold value ("YES" in step ST2170), it executes a warning process (step ST2180). In the warning process, the warning unit 123B instructs the output device 400 to issue a warning indicating that the evaluation is low. When issuing a warning command, the warning unit 123B may also instruct the output device 400 to present information such as the evaluation value itself and the degree to which the evaluation is low. After executing the warning process, the action evaluation device 100B then proceeds to an end determination process (step ST2190).

[0060] If the warning unit 123B determines that the evaluation value is not greater than or equal to the evaluation threshold, i.e., that the evaluation value is less than the evaluation threshold (step ST2170 "NO"), the action evaluation device 100B then proceeds to the termination determination process (step ST2190).

[0061] The action evaluation device 100B executes an end determination process (step ST2190). In the end determination process, a control unit (not shown) of the action evaluation device 100B determines whether to end the processing of the action evaluation device 100B. The control unit (not shown) determines whether to end the processing of the action evaluation device 100B, for example, in accordance with an external end command or an execution program. If the control unit (not shown) determines not to end the processing of the action evaluation device 100B, the process proceeds to step ST2110, and the process is repeated from step ST2110. If the control unit (not shown) determines to end the processing of the action evaluation device 100B, the action evaluation device 100B ends the processing.

[0062] The motion evaluation device of the present disclosure may further be configured, for example, as follows: A motion evaluation device comprising: an image acquisition unit that acquires an image from an imaging device that images the interior of the vehicle; and a motion determination unit that determines the motion of the occupant captured in the image. As a result, the present disclosure has a configuration that determines the motion of the occupant within the motion evaluation device, thereby achieving the effect of being able to evaluate the motion of the occupant even if a device for determining the motion of the occupant is not mounted on the vehicle. Furthermore, the present disclosure achieves the same effect as the above by applying the above configuration to a motion evaluation system or the above motion evaluation method.

[0063] The action evaluation device of the present disclosure may further be configured, for example, as follows: The action evaluation device includes a warning unit that issues a warning to the occupant using the evaluation value. This allows the present disclosure to achieve an effect of being able to warn the occupant according to the evaluation value. Furthermore, the present disclosure achieves the same effect as the above by applying the above configuration to the action evaluation system or the action evaluation method.

[0064] Embodiment 3. In embodiment 3, a model behavior model suited to a road environment can be shared for each similar road environment. In embodiment 3, among the components according to embodiment 3, duplicated descriptions of components similar to those according to embodiment 1 or embodiment 2 already described will be omitted as appropriate.

[0065] A configuration example of a movement evaluation device and a movement evaluation system including the device according to a third embodiment of the present disclosure will be described. FIG. 6 is a diagram showing a configuration example of a movement evaluation system 1C including a movement evaluation device 100C according to the third embodiment of the present disclosure. The movement evaluation system 1C includes the movement evaluation device 100C, a model behavior database 200C (database), a road environment database 210C (database), an output device 400C, and an imaging device 500C. The output device 400C is similar to the output devices 400A and 400B already described, and the imaging device 500C is similar to the imaging device 500B already described, so a redundant description of each will be omitted. Details of the road environment database 210C (database), the model behavior database 200C (database), and the movement evaluation device 100C will be described in that order.

[0066] The road environment database 210C (database) is a database that stores in advance the position information and the road environment information in association with each other.

[0067] The model behavior database 200C (database) is a database that stores in advance the road environment information and the model behavior model in association with each other.

[0068] The road environment database 210C and the model behavior database 200C constitute the databases of the present disclosure. The road environment database 210C and the model behavior database 200C may be in the form of an integrated database.

[0069] The action evaluation device 100C shown in Figure 6 is configured to include an image acquisition unit 121C, an action determination unit 122C, an action acquisition unit 111C, a position acquisition unit 112C, a road environment acquisition unit 131C, a model acquisition unit 113C, an evaluation value calculation unit 114C, and a warning unit 123C.

[0070] The image acquisition unit 121C has the same configuration as the image acquisition unit 121B already described, the motion determination unit 122C has the same configuration as the motion determination unit 122B already described, the motion acquisition unit 111C has the same configuration as the motion acquisition units 111A and 111B already described, the position acquisition unit 112C has the same configuration as the position acquisition units 112A and 112B already described, the evaluation value calculation unit 114C has the same configuration as the A and 114B already described, and the warning unit 123C has the same configuration as the warning unit 123B already described, and therefore description of each will be omitted to avoid duplication. The road environment acquisition unit 131C and the model acquisition unit 113C will be described.

[0071] The road environment acquisition unit 131C acquires road environment information corresponding to the position of the mobile object. The road environment acquisition unit 131C refers to a database (road environment database 210C) that stores position information and road environment information in association with each other in advance, and acquires the road environment information corresponding to the position acquired by the position acquisition unit 112C.

[0072] The model acquisition unit 113C acquires an exemplary behavior model corresponding to the road environment information. The model acquisition unit 113C refers to a database (an exemplary behavior database 200C) that stores road environment information and exemplary behavior models in association with each other, and acquires an exemplary behavior model corresponding to the road environment information acquired by the road environment acquisition unit 131C.

[0073] An example of processing by the action evaluating device according to the third embodiment of the present disclosure will be described. Fig. 7 is a flowchart showing an example of processing by the action evaluating device 100C according to the third embodiment of the present disclosure. The action evaluating device 100C starts the processing shown in Fig. 5 upon receiving a command from a control unit (not shown) or an external command. The action evaluating device 100C starts the processing when, for example, operation of a moving object is started.

[0074] First, the action evaluating apparatus 100C executes a video acquisition process (step ST3110) in the same manner as the video acquisition process (step ST2110) already described.

[0075] The action evaluating apparatus 100C then executes a action determining process (step ST3120) in the same manner as the action determining process (step ST2120) already described.

[0076] Next, the action evaluation apparatus 100C executes an action acquisition process (step ST3130) in the same manner as the action acquisition process (step ST2130) already described.

[0077] The action evaluation apparatus 100C then executes a position acquisition process (step ST3140) in the same manner as the position acquisition process (step ST2140) already described.

[0078] The action evaluation device 100C then executes a road environment acquisition process (step ST3150). In the road environment acquisition process, the road environment acquisition unit 131C of the action evaluation device 100C acquires road environment information corresponding to the position of the moving object. Specifically, the road environment acquisition unit 131C refers to a database (road environment database 210C) that pre-stores position information and road environment information in association with each other, and acquires the road environment information corresponding to the position acquired by the position acquisition unit 112C.

[0079] The action evaluation apparatus 100C then executes a model acquisition process (step ST3160). In the model acquisition process, the model acquisition unit 113C of the action evaluation apparatus 100C acquires an exemplary behavior model corresponding to the road environment information. Specifically, the model acquisition unit 113C refers to a database (an exemplary behavior database 200C) that pre-stores road environment information and exemplary behavior models in association with each other, and acquires the exemplary behavior model corresponding to the road environment information acquired by the road environment acquisition unit 131C.

[0080] The action evaluating apparatus 100C then executes an evaluation value calculation process (step ST3170) in the same manner as the evaluation value calculation process (step ST2160) already described.

[0081] The action evaluation device 100C then executes an evaluation value determination process ("evaluation value ≧ evaluation threshold", step ST3180) similar to the evaluation value determination process already described ("evaluation value ≧ evaluation threshold", step ST2170).

[0082] When the warning unit 123C of the action evaluation apparatus 100C determines that the evaluation value is equal to or greater than the evaluation threshold value (YES in step ST3180), it executes a warning process (step ST3190) similar to the warning process (step ST2180) already described. After executing the warning process, the action evaluation apparatus 100C then proceeds to an end determination process (step ST3200).

[0083] If the warning unit 123C determines that the evaluation value is not greater than or equal to the evaluation threshold, i.e., that the evaluation value is less than the evaluation threshold (step ST3180 "NO"), the action evaluation device 100C then proceeds to the termination determination process (step ST3200).

[0084] The action evaluation apparatus 100C executes the termination determination process (step ST3200) in the same manner as the termination determination process (step ST2190) already explained.

[0085] In the above-described embodiment, a model behavior model that can be shared for each road environment can be set, so that a more efficient system for evaluating behavior can be constructed.

[0086] The behavior evaluation device disclosed herein may further be configured, for example, as follows: The behavior evaluation device further includes a road environment acquisition unit that refers to a database in which the location information and the road environment information are associated and stored in advance, and acquires road environment information corresponding to the location acquired by the location acquisition unit, and the model acquisition unit refers to a database in which road environment information and an exemplary behavior model are associated and stored in advance, and acquires an exemplary behavior model corresponding to the road environment information acquired by the road environment acquisition unit. This allows the present disclosure to set an exemplary behavior model that is common to each road environment, thereby achieving the effect of building a more efficient system for behavior evaluation. Furthermore, the present disclosure provides the same effect as described above by applying the above configuration to a behavior evaluation system or the above behavior evaluation method.

[0087] Embodiment 4. In embodiment 4, an exemplary behavior model is described in which, among occupant behaviors such as cumulative inattentiveness determined by accumulating intermittent inattentiveness by an occupant, observing a direction that is acceptable depending on the road environment is used. In embodiment 4, duplicated descriptions of components according to embodiment 4 that are the same as components according to embodiment 1, embodiment 2, or embodiment 3 already described will be omitted as appropriate.

[0088] A configuration example of a movement evaluation device according to a fourth embodiment of the present disclosure and a movement evaluation system including the device will be described. FIG. 8 is a diagram illustrating a configuration example of a movement evaluation system 1D including a movement evaluation device 100D according to the fourth embodiment of the present disclosure. FIG. 9 is a first diagram illustrating an example of accumulated inattentive behavior handled by the movement evaluation device 100D according to the fourth embodiment. FIG. 10 is a second diagram illustrating an example of accumulated inattentive behavior handled by the movement evaluation device 100D according to the fourth embodiment. The movement evaluation system 1D includes the movement evaluation device 100D, a model behavior database 200D (database), an output device 400D, and an imaging device 500D. The model behavior database 200D (database) is similar to the model behavior databases 200A and 200B already described. The output device 400D is similar to the output devices 400A, 400B, and 400C already described. The imaging device 500D is similar to the imaging devices 500B and 500C already described. Therefore, description of each will be omitted to avoid duplication. The action evaluation device 100D will now be described in detail.

[0089] The action evaluation device 100D is configured to include a cumulative inattentive behavior judgment unit 141D, a cumulative inattentive behavior point acquisition unit 142D, an observation direction acquisition unit 143D, a cumulative inattentive behavior frequent point acquisition unit 144D, an allowable observation direction acquisition unit 145D, a model acquisition unit 113D, an evaluation value calculation unit 114D, and a warning unit 123D.

[0090] The evaluation value calculation unit 114D is configured in the same manner as the already described A, 114B, and 114C, and the warning unit 123D is configured in the same manner as the already described warning units 123B and 123C, so descriptions of each will be omitted to avoid duplication.

[0091] The cumulative inattentive driving determination unit 141D determines whether the occupant is in an inattentive state. The cumulative inattentive driving determination unit 141D acquires a cumulative value related to the occupant's intermittent inattentive driving and performs a cumulative inattentive driving determination, which determines whether the occupant is in an inattentive state, using the cumulative value and a predetermined threshold (accumulation threshold). The cumulative inattentive driving determination unit 141D, for example, analyzes video acquired from the imaging device 500D to acquire a cumulative value related to the occupant's intermittent inattentive driving. Each time the cumulative value is acquired, the cumulative inattentive driving determination unit 141D compares the cumulative value with the threshold and determines whether the cumulative value is equal to or greater than the threshold. The cumulative inattentive driving determination unit 141D determines that the occupant is in an inattentive state if the cumulative value is equal to or greater than the threshold. Alternatively, the cumulative inattentive driving determination unit 141D may be configured to determine whether the occupant is in an inattentive state using, for example, a known technique for determining cumulative inattentive driving other than the above-described determination method. The cumulative inattentive driving determination unit 141D outputs the result of the cumulative inattentive driving determination. The cumulative inattentive behavior determination unit 141D outputs a result of the cumulative inattentive behavior determination, for example, indicating that the cumulative value has reached a threshold value or more. Note that the cumulative inattentive behavior determination unit 141D may be configured to acquire a cumulative value related to intermittent inattentive behavior by the occupant from an occupant monitoring device.

[0092] Here, we will explain the cumulative inattentive behavior. Figure 9 illustrates a case where the mobile object 1000 is a vehicle, and the cumulative inattentive behavior frequent point is a point included in a merging area (merging road 2000, merged road 3000) on a motorway. When the mobile object 1000 is traveling from the merging road 2000 to the merged road 3000 in a traveling direction 1100 (or a predicted traveling direction), if the observation direction 1200 of the occupant of the mobile object 1000 is within the allowable observation range α (assuming the allowable observation range α is set in advance), it is usually determined that the occupant is not looking inattentively. However, in the situation illustrated in Figure 9, it is expected that the occupant will alternate between the observation direction 1200, which checks the road ahead of the merged road 3000, and the observation direction 1300, which checks the road behind the merged road 3000. In this case, the observation directions in the observation range β between the observation direction 1200 and the observation direction 1300 are accumulated, and if the accumulated inattentive behavior is determined mainly using the accumulated value, excessive accumulated inattentive behavior will be detected. In the present disclosure, for example, as shown in Figure 10, the range of acceptable observation directions can be used to perform behavior evaluation using an acceptable observation direction 4010 at an accumulated inattentive behavior frequent point, which is a point where accumulated inattentive behavior by occupants of each of the multiple moving bodies frequently occurs, in comparison with a normal acceptable observation direction 4000 (corresponding to the acceptable observation range α).

[0093] The cumulative inattentive point acquisition unit 142D acquires cumulative inattentive points, which are points at which it is determined that the occupant has engaged in a cumulative inattentive behavior. The cumulative inattentive point acquisition unit 142D acquires cumulative inattentive points based on the position of the vehicle when the cumulative inattentive state is determined to be present by the cumulative inattentive state determination unit 141D. The cumulative inattentive point is expressed by information such as the position at the moment when it is determined that the vehicle is in an inattentive state, or a representative value or average value of the positions over time during which values ​​related to inattentive behavior (cumulative values) are accumulated. The cumulative inattentive point acquisition unit 142D acquires information (position information) related to the positions of the cumulative inattentive points from, for example, a vehicle-mounted device mounted on the vehicle. Alternatively, the cumulative inattentive point acquisition unit 142D may be configured to calculate information (position information) related to the positions of the cumulative inattentive points using information acquired from, for example, a vehicle-mounted device mounted on the vehicle.

[0094] The observation direction acquisition unit 143D acquires the observation direction based on accumulated inattentive driving. The observation direction acquisition unit 143D acquires the observation direction, which is the direction in which the occupant of the mobile body is likely to be looking. Similar to the "observation direction" already described, the observation direction is represented by the occupant's facial direction, the occupant's line of sight, or information calculated from the occupant's facial direction and the occupant's line of sight. The observation direction may be represented by information indicating an area consisting of multiple observation directions. The observation direction may be represented by a representative value or average value of the area consisting of multiple observation directions. The observation direction acquisition unit 143D acquires the observation direction by analyzing, for example, video acquired from the image capture device 500D. Alternatively, the observation direction acquisition unit 143D may be configured to acquire the occupant's observation direction from an occupant monitoring device mounted on the mobile body. Alternatively, the observation direction acquisition unit 143D may be configured to acquire the observation direction by calculating the observation direction using information acquired from the occupant monitoring device mounted on the mobile body.

[0095] The cumulative inattentiveness frequent point acquisition unit 144D acquires cumulative inattentiveness frequent points, which are points where cumulative inattentiveness by occupants of each of multiple mobile bodies frequently occurs. The cumulative inattentiveness frequent points are location information indicating points where cumulative inattentiveness frequently occurs, generated based on information collected from multiple mobile bodies. The cumulative inattentiveness frequent points include those represented by information indicating an area consisting of multiple cumulative inattentiveness frequent points. The cumulative inattentiveness frequent points may also be represented by information such as a representative value or average value of the area consisting of multiple cumulative inattentiveness frequent points. The cumulative inattentiveness frequent point acquisition unit 144D acquires information related to cumulative inattentiveness frequent points output from a server device (a server device not shown, or a server device 600E according to Embodiment 5 described below) at a pre-stored fixed time or at predetermined intervals, for example. Alternatively, for example, when the cumulative inattentive state is determined by the cumulative inattentive state determination unit 141D, the cumulative inattentive state acquisition unit 144D uses the cumulative inattentive state acquisition unit 142D to acquire from the server device information related to the cumulative inattentive state points included in an area based on the cumulative inattentive state points. The information related to the cumulative inattentive state points includes location information indicating the locations of the cumulative inattentive state points. The cumulative inattentive state acquisition unit 144D extracts and outputs the location information indicating the locations of the cumulative inattentive state points included in the information related to the cumulative inattentive state points. The information related to the cumulative inattentive state points also includes allowable observation direction information indicating the allowable observation direction at the cumulative inattentive state points.

[0096] The allowable observation direction acquisition unit 145D acquires an allowable observation direction, which is a direction in which observation should be permitted for each cumulative inattentiveness occurrence point. The allowable observation direction is information generated based on information collected from multiple moving bodies and is represented by information indicating the direction in which observation should be permitted at each cumulative inattentiveness occurrence point. For example, if the moving body is a vehicle and the cumulative inattentiveness occurrence point is a point included in a merging area on a motorway, the allowable observation direction is a direction based on the position of the occupant and toward the road where the vehicle will merge (the merging road). Also, for example, if the moving body is a vehicle and the cumulative inattentiveness occurrence point is a point included in a merging area from a branch road to a main road, the allowable observation direction is a direction based on the position of the occupant and toward the road where the vehicle will merge (the merging road). Also, for example, if the moving body is a vehicle and the cumulative inattentive driving frequent point is a point included in the surrounding area of ​​an intersection, the allowable observation direction is a direction based on the occupant's position, toward the road connected to the intersection (the merging road). The allowable observation direction may be represented by information indicating an area consisting of multiple allowable observation directions. The allowable observation direction may be represented by a representative value or average value of the area consisting of multiple allowable observation directions. The allowable observation direction acquisition unit 145D acquires allowable observation direction information included in the information related to the cumulative inattentive driving frequent point acquired by the cumulative inattentive driving frequent point acquisition unit 144D.

[0097] The model acquisition unit 113D acquires an exemplary behavior model using the cumulative inattentiveness points, cumulative inattentiveness frequent points, observation direction, and allowable observation direction. When the cumulative inattentiveness points and the cumulative inattentiveness frequent points overlap, and when the observation direction acquired by the observation direction acquisition unit 143D overlaps with the allowable observation direction acquired by the allowable observation direction acquisition unit 145D, the model acquisition unit 113D acquires, as an exemplary behavior model, observation of the allowable observation direction at the position of the cumulative inattentiveness point. Specifically, the model acquisition unit 113D determines whether the cumulative inattentiveness points acquired by the cumulative inattentiveness point acquisition unit 142D and the cumulative inattentiveness frequent points acquired by the cumulative inattentiveness frequent point acquisition unit 144D overlap. This determination includes determining whether the cumulative inattentiveness points and the cumulative inattentiveness frequent points coincide, or determining whether an area based on the cumulative inattentiveness points and an area based on the cumulative inattentiveness frequent points at least partially overlap. Furthermore, the model acquisition unit 113D determines whether the observation direction acquired by the observation direction acquisition unit 143D overlaps with the allowable observation direction acquired by the allowable observation direction acquisition unit 145D. This determination includes determining whether the observation direction and the allowable observation direction match, or whether a directional region based on the observation direction overlaps at least partially with a directional region based on the cumulative inattentiveness frequent points. If the cumulative inattentiveness points acquired by the cumulative inattentiveness point acquisition unit 142D overlap with the cumulative inattentiveness frequent points acquired by the cumulative inattentiveness frequent point acquisition unit 144D, or if the observation direction acquired by the observation direction acquisition unit 143D overlaps with the allowable observation direction acquired by the allowable observation direction acquisition unit 145D, the model acquisition unit 113D acquires, as an exemplary behavior model, observation in the allowable observation direction at the location of the cumulative inattentiveness frequent point. This is because the allowable observation direction at the cumulative inattentiveness frequent points can be said to be the direction in which an occupant, such as a driver, should observe at the location of the moving body. Specifically, for example, if the cumulative frequent inattentive driving points include points that are included in a merging area on a motorway, the permissible observation direction is a direction based on the position of the occupant, and is the direction toward the road where the moving body will merge.By using such an allowable observation direction as a model behavior, the behavior evaluation device of the present disclosure can evaluate the behavior of a occupant driving a moving body by modeling behavior such as cumulative inattentive driving, with the observation direction being the direction toward the merging road in a merging area on a motorway.

[0098] A description will be given of a processing example of the action evaluating device according to the fourth embodiment of the present disclosure. Fig. 11 is a flowchart showing an example of processing of the action evaluating device 100D according to the fourth embodiment of the present disclosure.

[0099] The process shown in Fig. 11 is a method of evaluating an action using the action evaluation device 100D. The action evaluation device 100D starts the process shown in Fig. 11 when, for example, it acquires an image from the imaging device 500D. Note that the action evaluation device 100D may also start the process shown in Fig. 11 when it acquires a cumulative value related to cumulative inattentive behavior. In this case, the action evaluation device 100D acquires a cumulative value related to cumulative inattentive behavior from, for example, an occupant monitoring device (for example, the occupant monitoring device 300A already described).

[0100] The action evaluation device 100D first executes a cumulative inattentive state determination process (step ST4110). In the cumulative inattentive state determination process, the cumulative inattentive state determination unit 141D of the action evaluation device 100D acquires a cumulative value related to intermittent inattentive behavior by an occupant of the moving body, and performs a cumulative inattentive state determination, which is a determination of whether or not the occupant is in an inattentive state, using the cumulative value and a predetermined threshold value. For example, the cumulative inattentive state determination unit 141D first acquires a cumulative value by analyzing video and accumulating the cumulative inattentive state values. Alternatively, the cumulative inattentive state determination unit 141D acquires a cumulative value output from an occupant monitoring device. The cumulative inattentive state determination unit 141D then compares the cumulative value related to intermittent inattentive behavior with a pre-stored threshold value. If the cumulative value related to intermittent inattentive behavior exceeds the pre-stored threshold value, the cumulative inattentive state determination unit 141D determines that the occupant is in an inattentive state. The cumulative inattentive driving determination unit 141D then notifies the cumulative inattentive driving point acquisition unit 142D, the observation direction acquisition unit 143D, the cumulative inattentive driving frequent point acquisition unit 144D, and the allowable observation direction acquisition unit 145D that it has been determined that the driver is in a cumulative inattentive driving state.

[0101] When the action evaluation device 100D determines that the accumulated inattentive state is not present ("NO" in step ST4111), the action evaluation device 100D returns to the processing of step ST4110.

[0102] When the action evaluation device 100D determines that the cumulative inattentive state has occurred (step ST4111 "YES"), it then executes a cumulative inattentive point acquisition process (step ST4120). In the cumulative inattentive point acquisition process, the cumulative inattentive point acquisition unit 142D of the action evaluation device 100D acquires cumulative inattentive points based on the position of the mobile body when the cumulative inattentive state has been determined by the cumulative inattentive state determination unit 141D. When the cumulative inattentive point acquisition unit 142D acquires a notification indicating that the cumulative inattentive state has been determined from the cumulative inattentive state determination unit 141D, it acquires cumulative inattentive point information indicating the cumulative inattentive points. The cumulative inattentive point information is represented by positions or areas where cumulative inattentive states have occurred, based on the position of the mobile body acquired via a communication unit (not shown). The cumulative inattentive point acquisition unit 142D outputs the cumulative inattentive point information to the model acquisition unit 113D.

[0103] Next, the action evaluation device 100D executes an observation direction acquisition process (step ST4130). In the observation direction acquisition process, the observation direction acquisition unit 143D of the action evaluation device 100D acquires an observation direction, which is the direction from which an occupant of the moving body is likely to be observing. When the observation direction acquisition unit 143D acquires a notification indicating that a cumulative inattentive state has been determined from the cumulative inattentive state determination unit 141D, the observation direction acquisition unit 143D acquires observation direction information indicating the observation direction. The observation direction information indicates, for example, the facial direction of the occupant, the line of sight of the occupant, or the direction from which the occupant is likely to be observing based on the facial direction of the occupant and the line of sight of the occupant, acquired using video captured from the interior of the moving body. The observation direction acquisition unit 143D outputs the observation direction information to the model acquisition unit 113D.

[0104] Next, the action evaluation device 100D executes a cumulative inattentive point acquisition process (step ST4140). In the cumulative inattentive point acquisition process, the cumulative inattentive point acquisition unit 144D of the action evaluation device 100D acquires cumulative inattentive point occurrences, which are points where cumulative inattentive behavior by the occupants of each of the multiple mobile bodies frequently occurs. When the cumulative inattentive point acquisition unit 144D receives a notification indicating that a cumulative inattentive state has been determined from the cumulative inattentive state determination unit 141D, the cumulative inattentive point acquisition unit 144D acquires cumulative inattentive point information indicating the cumulative inattentive point occurrences. The cumulative inattentive point information indicates the locations or areas of the cumulative inattentive point occurrences, which are points where cumulative inattentive behavior by the occupants of each of the multiple mobile bodies frequently occurs. For example, the cumulative inattentive point acquisition unit 144D uses the cumulative inattentive point acquisition unit 142D to acquire information related to the cumulative inattentive point occurrences included in the area based on the cumulative inattentive point from, for example, a server device. The cumulative inattentive driving frequent point acquisition unit 144D outputs the cumulative inattentive driving frequent point information included in the acquired information relating to the cumulative inattentive driving frequent point to the model acquisition unit 113D.

[0105] Next, the action evaluation device 100D executes an allowable observation direction acquisition process (step ST4150). In the allowable observation direction acquisition process, the allowable observation direction acquisition unit 145D of the action evaluation device 100D acquires an allowable observation direction, which is a direction in which observation is permitted for each of the cumulative inattentive driving frequent locations. When the allowable observation direction acquisition unit 145D acquires a notification indicating that a cumulative inattentive driving state has been determined from the cumulative inattentive driving determination unit 141D, the allowable observation direction acquisition unit 145D acquires allowable observation direction information indicating the allowable observation direction included in the information related to the cumulative inattentive driving frequent locations acquired from the server device by the cumulative inattentive driving frequent location acquisition unit 144D. The allowable observation direction information indicates a direction or a directional area consisting of multiple directions that is permitted for observation at the cumulative inattentive driving frequent locations, which are locations where cumulative inattentive driving by occupants of each of multiple mobile bodies frequently occurs. The allowable observation direction acquisition unit 145D outputs the acquired allowable observation direction information to the model acquisition unit 113D.

[0106] Next, the action evaluation device 100D executes a permissible cumulative inattentive behavior determination process (step ST4160). In the permissible cumulative inattentive behavior determination process, the model acquisition unit 113D of the action evaluation device 100D determines whether the observation direction at the location of the cumulative inattentive behavior point is a direction in which observation is permissible (permissible observation direction). When the cumulative inattentive behavior point and the cumulative inattentive behavior point overlap, and when the observation direction acquired by the observation direction acquisition unit 143D overlaps with the permissible observation direction acquired by the permissible observation direction acquisition unit 145D, the model acquisition unit 113D acquires, as an exemplary behavior model, observation in the permissible observation direction at the location of the cumulative inattentive behavior point. Note that the overlapping states of the cumulative inattentive behavior point and the overlapping states of the observation direction acquired by the observation direction acquisition unit 143D and the permissible observation direction acquired by the permissible observation direction acquisition unit 145D are not limited to exact coincidence states, but also include, for example, states in which the difference value is equal to or less than a predetermined threshold.

[0107] If the model acquiring unit 113D determines that the observation direction does not overlap with the allowable observation direction (step ST4160 "NO"), the action evaluating device 100D then proceeds to the evaluation value calculation process (step ST4180).

[0108] If the model acquisition unit 113D determines that the observation direction and the permissible observation direction overlap ("YES" in step ST4160), the action evaluation device 100D then executes a permissible cumulative inattentive information output process (step ST4170). In the permissible cumulative inattentive information output process, the model acquisition unit 113D of the action evaluation device 100D acquires permissible cumulative inattentive information, including position information indicating the cumulative inattentive points and a permissible observation direction indicating the permissible observation direction, as an exemplary behavior model. The position information indicating the cumulative inattentive points and the permissible observation direction indicating the permissible observation direction correspond to an exemplary behavior model that indicates the behavior of observing the permissible observation direction at the position of the cumulative inattentive point.

[0109] Here, a detailed example of the processing of the model acquisition unit 113D will be described. FIG. 12 is a flowchart showing an example of the processing of the model acquisition unit 113D in the action evaluation device 100D according to the fourth embodiment of the present disclosure. The model acquisition unit 113D first compares the cumulative inattentiveness points indicated by the cumulative inattentiveness point information with the cumulative inattentiveness frequent points indicated by the cumulative inattentiveness frequent point information (step ST4161). The model acquisition unit 113D then determines whether the cumulative inattentiveness points and the cumulative inattentiveness frequent points overlap (step ST4162). If the cumulative inattentiveness points and the cumulative inattentiveness frequent points overlap ("YES" in step ST4162), the model acquisition unit 113D then compares the observation direction with the allowable observation direction (step ST4163). The model acquisition unit 113D then determines whether the observation direction and the allowable observation direction overlap (step ST4164).

[0110] If the observation direction and the permissible observation direction overlap ("YES" in step ST4164), the model acquisition unit 113D then executes a permissible cumulative inattentive behavior information output process (step ST4170). In the permissible cumulative inattentive behavior information output process, the model acquisition unit 113D acquires permissible cumulative inattentive behavior information, including position information indicating the cumulative inattentive behavior points and the permissible observation direction indicating the permissible observation direction, as an exemplary behavior model.

[0111] If the cumulative inattentive driving points and cumulative inattentive driving frequent points do not overlap ("NO" in step ST4162), or if the observation direction and the allowable observation direction do not overlap ("NO" in step ST4164), the model acquisition unit 113D executes a database reference process (step ST4165). In the database reference process, the model acquisition unit 113D references the exemplary behavior database 200, searches for an exemplary behavior model at the cumulative inattentive driving points, and outputs the exemplary behavior model according to the search results. If an exemplary behavior model is found, the model acquisition unit 113D acquires the exemplary behavior model and outputs the exemplary behavior model to the evaluation value calculation unit 114D. If an exemplary behavior model is not found, the process ends. When the model acquisition unit 113D ends the process, the action evaluation device 100D then proceeds to an evaluation value calculation process (step ST4180).

[0112] The processes in the allowable cumulative inattentive driving determination process (step ST4160), particularly the database reference process (step ST4165) and the allowable cumulative inattentive driving information output process (step ST4170), correspond to the model acquisition process of the present disclosure.

[0113] Returning to the description of Fig. 11 , the action evaluation device 100D then executes an evaluation value calculation process (step ST4180) similar to the evaluation value calculation process (e.g., step ST2160) already described. The evaluation value calculation unit 114D outputs the evaluation value as the calculation result to the warning unit 123D.

[0114] The action evaluation device 100D then executes an evaluation value judgment process ("evaluation value≧evaluation threshold value", step ST4190) in the same manner as the evaluation value judgment process (for example, step ST2170) already described.

[0115] When the warning unit 123D determines that the evaluation value is equal to or greater than the evaluation threshold value ("YES" in step ST4190), it executes a warning process (step ST4200). In the warning process, the warning unit 123D instructs the output device 400D to issue a warning indicating that the evaluation is low. When issuing a warning command, the warning unit 123D may also instruct the output device 400D to present information such as the evaluation value itself and the degree to which the evaluation is low. After the processing by the warning unit 123D is executed, the action evaluation device 100D then proceeds to an end determination process (step ST4210).

[0116] If the warning unit 123D determines that the evaluation value is not greater than or equal to the evaluation threshold, i.e., that the evaluation value is less than the evaluation threshold (step ST4190 "NO"), the action evaluation device 100D then proceeds to the termination determination process (step ST4210).

[0117] The action evaluation device 100D then executes an end determination process (step ST4210). In the end determination process, a control unit (not shown) of the action evaluation device 100D determines whether to end the processing of the action evaluation device 100D. The control unit (not shown) determines whether to end the processing of the action evaluation device 100D, for example, in accordance with an external end command or an execution program. If the control unit (not shown) determines not to end the processing of the action evaluation device 100D, the process proceeds to step ST4110, and the process is repeated from step ST4110. If the control unit (not shown) determines to end the processing of the action evaluation device 100D, the action evaluation device 100D ends the processing.

[0118] The action evaluation device of the present disclosure is further configured, for example, as follows. a cumulative inattentiveness determination unit that obtains a cumulative value related to intermittent inattentiveness by the occupant and performs a cumulative inattentiveness determination, which determines whether or not there is a cumulative inattentive state, using the cumulative value and a predetermined threshold value; a cumulative inattentiveness point acquisition unit that obtains cumulative inattentiveness points based on the position of the moving body when the cumulative inattentiveness determination unit determines that there is a cumulative inattentive state; an observation direction acquisition unit that obtains an observation direction, which is the direction in which the occupant of the moving body is likely to be observing; a cumulative inattentiveness frequent point acquisition unit that obtains cumulative inattentiveness frequent points, which are points where cumulative inattentiveness by the occupant of each of a plurality of moving bodies frequently occurs; and an allowable observation direction acquisition unit that obtains an allowable observation direction, which is the direction in which observation is allowable for each of the cumulative inattentiveness frequent points; wherein the model acquisition unit, when the cumulative inattentiveness points and the cumulative inattentiveness frequent points overlap and the observation direction acquired by the observation direction acquisition unit overlaps with the allowable observation direction, acquires as the exemplary behavior model observing the allowable observation direction at the position of the cumulative inattentiveness point. As a result, the present disclosure provides an effect that it is possible to perform a more accurate behavior evaluation regarding occupant behavior such as cumulative inattentiveness. Furthermore, the present disclosure provides the same effect as the above by applying the above configuration to a behavior evaluation system or the above behavior evaluation method.

[0119] The behavior evaluation device disclosed herein may further be configured, for example, as follows: When the cumulative inattentive behavior frequent point includes a point included in a merging area on a motorway, the behavior evaluation device is characterized in that the permissible observation direction is a direction based on the position of the occupant and is a direction toward the road where the moving body is merging. This allows the present disclosure to achieve an effect related to occupant behavior such as cumulative inattentive behavior, such as being able to tolerate cumulative inattentive behavior in a direction where it is preferable to observe, at a point included in a merging area on a motorway. Furthermore, the present disclosure achieves the same effect as the above by applying the above configuration to a behavior evaluation system or the above behavior evaluation method.

[0120] Embodiment 5. In embodiment 5, a form in which a motion evaluation device in each moving object and a server device cooperate to generate a database or update a database will be described. In embodiment 5, duplicated descriptions of components according to embodiment 5 that are the same as components according to embodiment 1, embodiment 2, embodiment 3, or embodiment 4 already described will be omitted as appropriate.

[0121] A description will be given of a motion evaluating device according to a fifth embodiment of the present disclosure. FIG. 13 is a diagram showing the relationship between a motion evaluating device 100E according to the fifth embodiment of the present disclosure and a server device 600E that generates a database used in the motion evaluating device 100E. The motion evaluating devices 100E (100E-1, 100E-2, 100E-3, ..., 100E-n (n is an integer of 2 or greater)) are mounted on mobile objects 700E (700E-1, 700E-2, 700E-3, ..., 700E-n (n is an integer of 2 or greater)) and are connected to the server device 600E via a network. The server device 600E acquires motion information from multiple mobile objects 700E (700E-1, 700E-2, 700E-3, ..., 700E-n) via the network. The motion evaluation devices 100E (100E-1, 100E-2, 100E-3, ..., 100E-n) mounted on the respective moving bodies 700E (700E-1, 700E-2, 700E-3, ..., 700E-n) provide motion information to the server device 600E.

[0122] An example configuration of an action evaluation device and an action evaluation system including the device according to a fifth embodiment of the present disclosure will be described. FIG. 14 is a diagram showing a first example configuration of an action evaluation system 1E including an action evaluation device 100E according to the fifth embodiment of the present disclosure. FIG. 15 is a diagram showing a second example configuration of an action evaluation system 1E' including an action evaluation device 100E' according to the fifth embodiment of the present disclosure. FIG. 16 is a diagram showing a third example configuration of an action evaluation system 1E" including an action evaluation device 100E" according to the fifth embodiment of the present disclosure. The action evaluation system 1E (1E', 1E") includes an action evaluation device 100E (100E', 100E"), an output device 400E, an imaging device 500E, and a server device 600E. The output device 400E is similar to the output devices 400A, 400B, 400C, and 400D already described, and the imaging device 500E is similar to the imaging devices 500B, 500C, and 500D already described, so their descriptions will be omitted to avoid duplication. Details of each will be described in the order of the action evaluation devices (100E, 100E', and 100E") and the server device 600E.

[0123] The action evaluating apparatus according to the fifth embodiment is configured to include the same components as any of the action evaluating apparatuses 100A, 100B, 100C, and 100D already described in the respective embodiments. For example, the action evaluating apparatus 100E shown in FIG. 14 includes the same components as the action evaluating apparatus 100B already described in the second embodiment. For example, the action evaluating apparatus 100E' shown in FIG. 15 includes the same components as the action evaluating apparatus 100C already described in the third embodiment. For example, the action evaluating apparatus 100E" shown in FIG. 16 includes the same components as the action evaluating apparatus 100D already described in the fourth embodiment. In addition to this configuration, the action evaluating apparatus according to the fifth embodiment further includes a action information providing unit 151E. For example, each of the action evaluating apparatuses 100E, 100E', and 100E" shown in the respective figures further includes a action information providing unit 151E. Of the internal configurations of the action evaluating devices 100E, 100E', and 100E" according to embodiment 5, the components similar to those of the action evaluating devices 100A, 100B, 100C, and 100D already described in the respective embodiments have already been described in the respective embodiments, and therefore will not be described here. The action information providing unit 151E will be described.

[0124] The action information providing unit 151E provides the action information to a server device 600E that generates the database of the present disclosure. Specifically, the action information providing unit 151E in the action evaluation device 100E, 100E' outputs to the server device 600E action information that is a combination of the action-related information acquired by the action acquisition unit 111E and the position information acquired by the position acquisition unit 112E. Also specifically, the action information providing unit 151E in the action evaluation device 100E" outputs to the server device 600E action information that is a combination of the position information and the action-related information, with the accumulated inattentive point acquisition unit 142E as position information and the observation directions acquired in chronological order by the observation direction acquisition unit as action-related information, respectively.

[0125] The server device 600E generates the database of the present disclosure. The server device 600E determines an exemplary behavior model corresponding to the road environment for each location based on the behavior information, and generates a database that associates the location, the road environment, and the exemplary behavior model. Furthermore, if a database has already been generated, the server device 600E updates the generated database using the determined exemplary behavior model. The database generated by the server device 600E is the database already described in each embodiment, specifically, at least one of the exemplary behavior databases 200A, 200B, 200C, 200D, and 200E (databases) or the road environment database 210C (database). The server device 600E provides the generated or updated database to the behavior evaluation devices 100E, 100E′, and 100E″ of each moving object 700E (700E-1, 700E-2, 700E-3, ..., 700E-n). Alternatively, the server device 600E may be configured to use the generated or updated database to cooperate with the action evaluation devices 100E, 100E', 100E" of each moving body 700E (700E-1, 700E-2, 700E-3, ..., 700E-n) and provide information in response to a request from the action evaluation devices 100E, 100E', 100E". The information provided by the server device 600E to the action evaluation devices 100E, 100E', 100E" may be a model behavior model or road environment information.

[0126] A configuration example of a server device according to embodiment 5 will be described. Server devices 600E, 600E′, and 600E″ shown in Fig. 14, 15, and 16, respectively, include a motion information collecting unit 601E and a model behavior database 200E (database).

[0127] The motion information collection unit 601E collects motion information from a plurality of moving bodies 700E (700E-1, 700E-2, 700E-3, ..., 700E-n), associates position information with an exemplary behavior model corresponding to the road environment based on the collected motion information, and stores the associated information in a database (at least one of the exemplary behavior databases 200A, 200B, 200C, 200D, and 200E (databases) or the road environment database 210C (database)). That is, the position information and the exemplary behavior model associated with the database according to the fifth embodiment of the present disclosure are each generated based on motion information including positions and occupant motions collected from a plurality of moving bodies 700E (700E-1, 700E-2, 700E-3, ..., 700E-n).

[0128] A processing example of the action evaluating apparatus according to the fifth embodiment of the present disclosure will be described. FIG. 17 is a flowchart showing an example of processing of the action information providing unit 151 in the action evaluating apparatus 100E, 100E′, 100E″ according to the fifth embodiment of the present disclosure. The action information providing unit 151E starts the processing shown in FIG. 17 upon receiving information from, for example, an external source. Alternatively, the action information providing unit 151E starts the processing shown in FIG. 17 based on, for example, an instruction from a control unit (not shown).

[0129] The motion information providing unit 151E executes a motion information acquisition determination process (step ST5110). In the motion information acquisition determination process, the motion information providing unit 151E determines whether motion information has been acquired. If the motion information providing unit 151E determines that motion information has not been acquired (step ST5110 "NO"), it continues the process of step ST5110 until motion information is acquired.

[0130] When the motion information providing unit 151E determines that the motion information has been acquired in the motion information acquisition determination process ("YES" in step ST5110), it then executes the motion information providing process (step ST5120). In the motion information providing process, the motion information providing unit 151E outputs, to the server device 600E, motion information that is a combination of position information and information related to the motion of the occupant. Specifically, the motion information providing unit 151E in the motion evaluation device 100E, 100E' outputs, to the server device 600E, motion information that is a combination of information related to the motion acquired by the motion acquiring unit 111E and position information acquired by the position acquiring unit 112E. Specifically, the action information providing unit 151E in the action evaluation device 100E'' uses the accumulated inattentive point acquisition unit 142E as location information and the observation direction acquired in chronological order by the observation direction acquisition unit as information related to the action, and outputs action information, which is information combining location information and information related to the action, to the server device 600E.

[0131] After executing the motion information providing process, the motion information providing unit 151E ends the process shown in FIG.

[0132] An example of processing by the server device according to the fifth embodiment of the present disclosure will be described. FIG. 18 is a flowchart showing an example of processing by the server device 600E when motion information is provided from the motion evaluation devices 100E, 100E', and 100E" according to the fifth embodiment of the present disclosure. The server device 600E starts the processing shown in FIG. 18 when, for example, it receives information from the outside. Alternatively, the server device 600E starts the processing shown in FIG. 18 based on, for example, an instruction from a control unit (not shown) in the server device 600E. The server device 600E executes a motion information acquisition determination process (step ST5210). In the motion information acquisition determination process, the motion information collection unit 601E of the server device 600E determines whether motion information has been acquired.

[0133] If the motion information collecting unit 601E determines that it has not acquired motion information ("NO" in step ST5210), it continues the process of step ST5210 until it acquires motion information. If the motion information collecting unit 601E determines that it has acquired motion information ("YES" in step ST5210), the server device 600E then executes a model behavior model storage process (step ST5220). In the model behavior model storage process, the motion information collecting unit 601E of the server device 600E collects motion information from a plurality of moving objects 700E (700E-1, 700E-2, 700E-3, ..., 700E-n), associates position information with a model behavior model corresponding to the road environment based on the collected motion information, and stores the associated information in a database (at least one of the model behavior databases 200A, 200B, 200C, 200D, and 200E (databases) or the road environment database 210C (database)). Specifically, the motion information collecting unit 601E acquires motion information from the motion evaluation devices 100E, 100E', and 100E" of each moving body 700E (700E-1, 700E-2, 700E-3, ..., 700E-n) and stores the motion information. The motion information collecting unit 601E aggregates the position information and motion-related information included in the stored motion information to determine an exemplary behavior model for each position. For example, when the number of identical motions for each position reaches or exceeds a predetermined threshold (motion count threshold), the motion information collecting unit 601E determines the motion as an exemplary behavior model that shows an exemplary behavior according to the road environment. The motion information collecting unit 601E associates the determined exemplary behavior model with the position information and stores the association data in a database.

[0134] After executing the model behavior model storage process, the action information collecting unit 601E ends the process shown in FIG.

[0135] The action evaluation device of the present disclosure may further be configured, for example, as follows: The action evaluation device is characterized in that the location information and the exemplary behavior model associated with the database are each generated based on action information including positions and occupant actions collected from multiple mobile objects. This allows the present disclosure to use an exemplary behavior model based on a large amount of actual data indicating actual actions at each location, thereby achieving the effect of improving the accuracy of driving evaluation or action evaluation. Furthermore, the present disclosure achieves the same effect as the above by applying the above configuration to an action evaluation system or the above action evaluation method.

[0136] The action evaluation device of the present disclosure may further be configured, for example, as follows: The action evaluation device further includes: an action information providing unit that outputs the action information to a server device that generates the database. This allows the present disclosure to use an exemplary behavior model based on a large amount of actual data showing actual actions at each location, thereby achieving the effect of improving the accuracy of driving evaluation or action evaluation. Furthermore, the present disclosure achieves the same effect as the above by applying the above configuration to an action evaluation system or the above action evaluation method.

[0137] Here, a hardware configuration for realizing the functions of the present disclosure will be described. Fig. 19 is a diagram illustrating a first example of a hardware configuration for realizing the functions of the configuration of the present disclosure. Fig. 20 is a diagram illustrating a second example of a hardware configuration for realizing the functions of the configuration of the present disclosure. The action evaluation devices 100, 100A, 100B, 100C, 100D, 100E (100E-1, 100E-2, 100E-3, ..., 100E-n (n is an integer of 2 or more)), 100E' (100E'-1, 100E'-2, 100E'-3, ..., 100E'-n (n is an integer of 2 or more)), 100E'' (100E''-1, 100E''-2, 100E''-3, ..., 100E''-n (n is an integer of 2 or more)), occupant monitoring device 300A, and server device 600E of the present disclosure are each realized by hardware such as shown in FIG. 19 or FIG. 20 .

[0138] As shown in FIG. 19 , each of the action evaluation devices 100, 100A, 100B, 100C, 100D, 100E, 100E′, and 100E″ is configured, for example, with a processor 10001, a memory 10002, an input / output interface 10003, and a communication circuit 10004. The processor 10001 and the memory 10002 are, for example, installed in a computer. The memory 10002 stores the computer, which includes: motion acquisition units 111, 111A, 111B, 111C, 111E, and 111E'; position acquisition units 112, 112A, 112B, 112C, 112E, and 112E'; model acquisition units 113, 113A, 113B, 113C, 113D, 113E, 113E', and 113E''; evaluation value calculation units 114, 114A, 114B, 114C, 114D, and 114E; image acquisition units 121B, 121C, 121E, 121E', operation judgment units 122B, 122C, 122E, 122E', warning units 123B, 123C, 123D, 123E, road environment acquisition unit 131C, cumulative inattentive driving judgment unit 141D, cumulative inattentive driving point acquisition unit 142D, observation direction acquisition unit 143D, cumulative inattentive driving frequent point acquisition unit 144D, allowable observation direction acquisition unit 145D, operation information provision unit 151E, and programs for functioning as a control unit not shown are stored. The processor 10001 reads out and executes the program stored in the memory 10002, whereby the motion acquisition units 111, 111A, 111B, 111C, 111E, and 111E', the position acquisition units 112, 112A, 112B, 112C, 112E, and 112E', the model acquisition units 113, 113A, 113B, 113C, 113D, 113E, 113E', and 113E'', the evaluation value calculation units 114, 114A, 114B, 114C, 114D, and 114E, and the image acquisition unit 12 The functions of 1B, 121C, 121E, 121E', operation judgment units 122B, 122C, 122E, 122E', warning units 123B, 123C, 123D, 123E, road environment acquisition unit 131C, cumulative inattentive driving judgment units 141D, 141E, cumulative inattentive driving point acquisition units 142D, 142E, observation direction acquisition units 143D, 143E, cumulative inattentive driving frequent point acquisition units 144D, 144E, allowable observation direction acquisition units 145D, 145E, operation information provision unit 151E, and a control unit not shown are realized.Furthermore, a storage unit (not shown) is realized by the memory 10002 or another memory (not shown). Furthermore, a communication unit (not shown) is realized by the communication circuit 10004.

[0139] Similarly, the occupant monitoring device 300A is configured with, for example, a processor 10001, a memory 10002, an input / output interface 10003, and a communication circuit 10004. The processor 10001 and the memory 10002 are mounted on, for example, a computer. The memory 10002 stores programs that cause the computer to function as the image acquisition unit 301A, the motion determination unit 302A, and a control unit (not shown). The processor 10001 reads and executes the programs stored in the memory 10002 to realize the functions of the image acquisition unit 301A, the motion determination unit 302A, and the control unit (not shown). The memory 10002 or another memory (not shown) realizes a storage unit (not shown) in the occupant monitoring device 300A. The communication circuit 10004 realizes a communication unit (not shown) in the occupant monitoring device 300A.

[0140] Similarly, the server device 600E is configured with, for example, a processor 10001, a memory 10002, an input / output interface 10003, and a communication circuit 10004. The processor 10001 and the memory 10002 are installed in, for example, a computer. The memory 10002 stores a program for causing the computer to function as a motion information collecting unit 601E and a control unit (not shown). The processor 10001 reads and executes the program stored in the memory 10002, thereby realizing the functions of the motion information collecting unit 601E and the control unit (not shown). The memory 10002 or another memory (not shown) also realizes a storage unit (not shown) in the server device 600E. The communication circuit 10004 also realizes a communication unit (not shown) in the server device 600E.

[0141] The processor 10001 is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a microprocessor, a microcontroller, or a DSP (Digital Signal Processor). The memory 10002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory) or flash memory, or a magnetic disk such as a hard disk or flexible disk, or an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc), or a magneto-optical disk. The processor 10001 and the memory 10002 or the communication circuit 10004 are connected in a state capable of transmitting data to each other. The processor 10001, memory 10002, and communication circuit 10004 are connected via an input / output interface 10003 so as to be capable of transmitting data to and from other hardware.

[0142] Or, in the action evaluation devices 100, 100A, 100B, 100C, 100D, 100E, 100E', 100E'', the action acquisition units 111, 111A, 111B, 111C, 111E, 111E', the position acquisition units 112, 112A, 112B, 112C, 112E, 112E', the model acquisition units 113, 113A, 113B, 113C, 113D, 113E, 113E', 113E'', the evaluation value calculation units 114, 114A, 114B, 114C, 114D, 114E, the image acquisition units 121B, 121C, 121E, The functions of 121E', operation judgment units 122B, 122C, 122E, 122E', warning units 123B, 123C, 123D, 123E, road environment acquisition unit 131C, cumulative inattentive driving judgment units 141D, 141E, cumulative inattentive driving point acquisition units 142D, 142E, observation direction acquisition units 143D, 143E, cumulative inattentive driving frequent point acquisition units 144D, 144E, allowable observation direction acquisition units 145D, 145E, operation information provision unit 151E, and a control unit not shown may be realized by a dedicated processing circuit 20001, as shown in FIG. 20.

[0143] Similarly, the functions of the image acquisition unit 301A, the operation determination unit 302A, and the control unit (not shown) in the occupant monitoring device 300A may be realized by a dedicated processing circuit 20001.

[0144] Similarly, the functions of the operation information collecting circuitry 601E and a control unit (not shown) in the server device 600E may be realized by a dedicated processing circuit 20001.

[0145] The processing circuit 20001 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field-Programmable Gate Array), a SoC (System-on-a-Chip), or a system LSI (Large-Scale Integration), etc. In addition, a storage unit (not shown) is realized by the memory 20002 or another memory (not shown). The memory 20002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory), or flash memory, or a magnetic disk such as a hard disk or flexible disk, or an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc), or a magneto-optical disk. The communication circuit 20004 implements a communication unit (not shown). The processing circuit 20001 and the memory 20002 or the communication circuit 20004 are connected in a state where they can transmit data to each other. In addition, the processing circuit 20001, the memory 20002, and the communication circuit 20004 are connected in a state where they can transmit data to other hardware via the input / output interface 20003.In addition, in the action evaluation devices 100, 100A, 100B, 100C, 100D, 100E, 100E', and 100E'', the action acquisition units 111, 111A, 111B, 111C, 111E, and 111E', the position acquisition units 112, 112A, 112B, 112C, 112E, and 112E', the model acquisition units 113, 113A, 113B, 113C, 113D, 113E, 113E', and 113E'', the evaluation value calculation units 114, 114A, 114B, 114C, 114D, and 114E, the image acquisition units 121B, 121C, and 121D The functions of the image acquisition unit 301A, the image acquisition unit 302A, and the control unit (not shown) in the occupant monitoring device 300A may be realized by separate processing circuits or may be realized together in a processing circuit. Similarly, the functions of the operation information collecting unit 601E and the control unit (not shown) in the server device 600E may be realized by separate processing circuits, or may be realized together by a processing circuit.

[0146] Or, in the action evaluation devices 100, 100A, 100B, 100C, 100D, 100E, 100E', 100E'', the action acquisition units 111, 111A, 111B, 111C, 111E, 111E', the position acquisition units 112, 112A, 112B, 112C, 112E, 112E', the model acquisition units 113, 113A, 113B, 113C, 113D, 113E, 113E', 113E'', the evaluation value calculation units 114, 114A, 114B, 114C, 114D, 114E, the image acquisition units 121B, 121C, 121E, 121E', and the action determination unit 122B , 122C, 122E, 122E', warning unit 123B, 123C, 123D, 123E, road environment acquisition unit 131C, cumulative inattentive driving judgment unit 141D, 141E, cumulative inattentive driving point acquisition unit 142D, 142E, observation direction acquisition unit 143D, 143E, cumulative inattentive driving frequent point acquisition unit 144D, 144E, allowable observation direction acquisition unit 145D, 145E, operation information provision unit 151E, and some of the functions of a control unit not shown may be realized by processor 10001 and memory 10002, and the remaining functions may be realized by processing circuit 20001. Similarly, in the occupant monitoring device 300A, some functions of the image acquisition unit 301A, the action determination unit 302A, and a control unit (not shown) may be realized by the processor 10001 and the memory 10002, and the remaining functions may be realized by the processing circuit 20001. Similarly, in the server device 600E, some functions of the action information collection unit 601E and a control unit (not shown) may be realized by the processor 10001 and the memory 10002, and the remaining functions may be realized by the processing circuit 20001.

[0147] It should be noted that, within the scope of this disclosure, the embodiments may be freely combined, any of the components of the embodiments may be modified, or any of the components of the embodiments may be omitted. For example, the components of the action evaluation device according to the fourth embodiment may be combined with components of other embodiments.

[0148] The present disclosure is capable of performing a more preferable driving evaluation than conventional methods, and is therefore suitable for use in a behavior evaluation device that evaluates the behavior of a driver driving a moving body such as a vehicle.

[0149] 1A, 1B, 1C, 1D, 1E Movement evaluation system, 100, 100A, 100B, 100C, 100D, 100E (100E-1, 100E-2, 100E-3, ..., 100E-n (n is an integer of 2 or more)), 100E' (100E'-1, 100E'-2, 100E'-3, ..., 100E'-n (n is an integer of 2 or more)), 100E'' (100E''-1, 100E''-2, 100E''-3, ..., 100E''-n (n is an integer of 2 or more)) Movement evaluation device, 111, 111A, 111B, 111C, 111E, 111E' Action acquisition unit, 112, 112A, 112B, 112C, 112E, 112E′ Position acquisition unit, 113, 113A, 113B, 113C, 113D, 113E, 113E′, 113E″ Model acquisition unit, 114, 114A, 114B, 114C, 114D, 114E, 114E′, 114E″ Evaluation value calculation unit, 121B, 121C, 121E, 121E′ Video acquisition unit, 122B, 122C, 122E, 122E′ Action determination unit, 123B, 123C, 123D, 123E Warning unit, 131C Road environment acquisition unit, 141D, 141E Accumulative inattentive behavior determination unit, 142D, 142E Accumulative inattentive point acquisition unit, 143D, 143E Observation direction acquisition unit, 144D, 144E Accumulative inattentive frequent point acquisition unit, 145D, 145E Allowable observation direction acquisition unit (corresponding to "model acquisition unit"), 151E Action information provision unit, 200A, 200B, 200C, 200D, 200E Model behavior database (database), 210C Road environment database (database), 300A Occupant monitoring device, 301A Video acquisition unit, 302A Action determination unit, 400A, 400B, 400C, 400D, 400E Output device, 500B, 500C, 500D, 500E Imaging device, 600E Server device, 601E Action information collection unit, 700E (700E-1, 700E-2, 700E-3, ...,700E-n (n is an integer of 2 or more)) Mobile body, 1000 vehicle (mobile body), 1100 direction of travel (predicted direction of travel), 1200 normal allowable observation direction, 1300 allowable observation direction at cumulative inattentiveness frequent points, 2000 merging lane, 3000 merging lane, 4000 normal allowable observation direction, 4010 allowable observation direction at cumulative inattentiveness frequent points, 10001 processor, 10002 memory, 10003 input / output interface, 10004 communication circuit, 20001 processing circuit, 20002 memory, 20003 input / output interface, 20004 communication circuit. ,

Claims

1. A behavior evaluation device comprising: a behavior acquisition unit that acquires the behavior of a occupant driving a moving body; a position acquisition unit that acquires the position of the moving body; a model acquisition unit that acquires an exemplary behavior model corresponding to the road environment at the position of the moving body using a database in which position information is associated with an exemplary behavior model showing exemplary behavior corresponding to the road environment and is pre-stored; and an evaluation value calculation unit that calculates an evaluation value of the behavior based on the behavior acquired by the behavior acquisition unit and the exemplary behavior model acquired by the model acquisition unit.

2. The motion evaluation device according to claim 1, characterized in that the motion acquired by the motion acquisition unit is the motion of the occupant determined using video footage of the interior of the vehicle.

3. The motion evaluation device according to claim 1 or 2, characterized in that the road environment includes at least one of the following: a motorway, a merging area of ​​a motorway, near an intersection, near a pedestrian crossing, the presence or absence of a traffic light, the shape of the road, the width of the road, the degree of vehicle congestion, or the degree of pedestrian congestion.

4. The movement evaluation device according to any one of claims 1 to 3, characterized in that the model behavior model includes at least one of the following: observation direction, cumulative inattentiveness, or position of a body part.

5. The motion evaluation device according to claim 4, characterized in that the observation direction includes at least one of the direction of the passenger's face or the direction of the passenger's line of sight.

6. A motion evaluation device as described in any one of claims 1 to 5, comprising: an image acquisition unit that acquires images from an imaging device that images the interior of the moving body; and a motion judgment unit that judges the motion of the occupant captured in the images.

7. A behavior evaluation device as described in any one of claims 1 to 6, further comprising a road environment acquisition unit that refers to a database in which the location information and the road environment information are pre-stored in association with each other, and acquires road environment information corresponding to the location acquired by the location acquisition unit, wherein the model acquisition unit refers to a database in which road environment information and an exemplary behavior model are pre-stored in association with each other, and acquires an exemplary behavior model corresponding to the road environment information acquired by the road environment acquisition unit.

8. The vehicle further comprises a cumulative inattentiveness determination unit that obtains a cumulative value related to intermittent inattentiveness by the occupant and performs a cumulative inattentiveness determination, which is a determination of whether or not there is a cumulative inattentive state, using the cumulative value and a predetermined threshold value; a cumulative inattentiveness point acquisition unit that acquires a cumulative inattentiveness point based on the position of the moving body when the cumulative inattentiveness determination unit determines that there is a cumulative inattentive state; an observation direction acquisition unit that acquires an observation direction that is a direction in which the occupant of the moving body is likely to be observing; a cumulative inattentiveness frequent point acquisition unit that acquires cumulative inattentiveness frequent points that are points where cumulative inattentiveness by the occupant of each of a plurality of moving bodies frequently occurs; and an allowable observation direction acquisition unit that acquires an allowable observation direction that is a direction that should be allowed to be observed for each of the cumulative inattentiveness frequent points, wherein the model acquisition unit, when the cumulative inattentiveness point and the cumulative inattentiveness frequent point overlap and when the observation direction acquired by the observation direction acquisition unit overlaps with the allowable observation direction, acquires observing the allowable observation direction at the position of the cumulative inattentiveness point as the exemplary behavior model. The action evaluation device according to claim 1 .

9. The action evaluation device described in claim 8, characterized in that, when the cumulative frequent inattentive driving points include a point that is included in a merging area of ​​a motorway, the permissible observation direction is a direction based on the position of the occupant and is a direction toward the merging road where the moving body will merge.

10. A motion evaluation device as described in any one of claims 1 to 9, characterized in that the location information and the exemplary behavior model associated with the database are each generated based on motion information including positions collected from multiple moving objects and the motions of the occupants.

11. The action evaluation device according to claim 10, further comprising a action information providing unit that outputs the action information to a server device that generates the database.

12. The action evaluation device according to any one of claims 1 to 11, further comprising: a warning unit that issues a warning to the occupant using the evaluation value.

13. A movement evaluation method comprising: a movement acquisition step in which a movement acquisition unit of a movement evaluation device acquires the movement of a occupant driving a moving body; a position acquisition step in which a position acquisition unit of the movement evaluation device acquires the position of the moving body; a model acquisition step in which a model acquisition unit of the movement evaluation device acquires an exemplary behavior model corresponding to the road environment at the position of the moving body using a database in which position information is associated with an exemplary behavior model showing exemplary movements corresponding to the road environment and is pre-stored; and an evaluation value calculation step in which an evaluation value calculation unit of the movement evaluation device calculates an evaluation value of the movement based on the movement acquired by the movement acquisition unit and the exemplary behavior model.

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